diff --git a/pandas/_testing/asserters.py b/pandas/_testing/asserters.py
index da4a73ce1c80f..de28342c0d526 100644
--- a/pandas/_testing/asserters.py
+++ b/pandas/_testing/asserters.py
@@ -75,8 +75,8 @@ def assert_almost_equal(
     right : object
     check_dtype : bool or {'equiv'}, default 'equiv'
         Check dtype if both a and b are the same type. If 'equiv' is passed in,
-        then `RangeIndex` and `Int64Index` are also considered equivalent
-        when doing type checking.
+        then `RangeIndex` and `NumericIndex` with int64 dtype are also considered
+        equivalent when doing type checking.
     rtol : float, default 1e-5
         Relative tolerance.
 
@@ -197,7 +197,7 @@ def assert_index_equal(
     exact : bool or {'equiv'}, default 'equiv'
         Whether to check the Index class, dtype and inferred_type
         are identical. If 'equiv', then RangeIndex can be substituted for
-        Int64Index as well.
+        NumericIndex with an int64 dtype as well.
     check_names : bool, default True
         Whether to check the names attribute.
     check_exact : bool, default True
@@ -511,7 +511,7 @@ def assert_interval_array_equal(
     exact : bool or {'equiv'}, default 'equiv'
         Whether to check the Index class, dtype and inferred_type
         are identical. If 'equiv', then RangeIndex can be substituted for
-        Int64Index as well.
+        NumericIndex with an int64 dtype as well.
     obj : str, default 'IntervalArray'
         Specify object name being compared, internally used to show appropriate
         assertion message
diff --git a/pandas/compat/pickle_compat.py b/pandas/compat/pickle_compat.py
index b3eb5b1bfdcde..2fbd3a6cdb046 100644
--- a/pandas/compat/pickle_compat.py
+++ b/pandas/compat/pickle_compat.py
@@ -94,8 +94,8 @@ def load_reduce(self):
     ("pandas.indexes.base", "_new_Index"): ("pandas.core.indexes.base", "_new_Index"),
     ("pandas.indexes.base", "Index"): ("pandas.core.indexes.base", "Index"),
     ("pandas.indexes.numeric", "Int64Index"): (
-        "pandas.core.indexes.numeric",
-        "Int64Index",
+        "pandas.core.indexes.base",
+        "Index",  # updated in 50775
     ),
     ("pandas.indexes.range", "RangeIndex"): ("pandas.core.indexes.range", "RangeIndex"),
     ("pandas.indexes.multi", "MultiIndex"): ("pandas.core.indexes.multi", "MultiIndex"),
@@ -119,7 +119,7 @@ def load_reduce(self):
         "TimedeltaIndex",
     ),
     ("pandas.indexes.numeric", "Float64Index"): (
-        "pandas.core.indexes.numeric",
+        "pandas.core.indexes.base",
         "Index",  # updated in 50775
     ),
     # 50775, remove Int64Index, UInt64Index & Float64Index from codabase
diff --git a/pandas/conftest.py b/pandas/conftest.py
index 1a410f87c8552..2e9638036eec5 100644
--- a/pandas/conftest.py
+++ b/pandas/conftest.py
@@ -520,7 +520,7 @@ def multiindex_year_month_day_dataframe_random_data():
     """
     tdf = tm.makeTimeDataFrame(100)
     ymd = tdf.groupby([lambda x: x.year, lambda x: x.month, lambda x: x.day]).sum()
-    # use Int64Index, to make sure things work
+    # use int64 Index, to make sure things work
     ymd.index = ymd.index.set_levels([lev.astype("i8") for lev in ymd.index.levels])
     ymd.index.set_names(["year", "month", "day"], inplace=True)
     return ymd
diff --git a/pandas/core/algorithms.py b/pandas/core/algorithms.py
index aa9d1c8152019..a7fa77d1ff0ee 100644
--- a/pandas/core/algorithms.py
+++ b/pandas/core/algorithms.py
@@ -1305,7 +1305,7 @@ def __init__(self, obj: DataFrame, n: int, keep: str, columns: IndexLabel) -> No
 
     def compute(self, method: str) -> DataFrame:
 
-        from pandas.core.api import Int64Index
+        from pandas.core.api import NumericIndex
 
         n = self.n
         frame = self.obj
@@ -1333,7 +1333,7 @@ def get_indexer(current_indexer, other_indexer):
         original_index = frame.index
         cur_frame = frame = frame.reset_index(drop=True)
         cur_n = n
-        indexer = Int64Index([])
+        indexer = NumericIndex([], dtype=np.int64)
 
         for i, column in enumerate(columns):
             # For each column we apply method to cur_frame[column].
diff --git a/pandas/core/api.py b/pandas/core/api.py
index 3d2547fcea230..0dffa681acfc1 100644
--- a/pandas/core/api.py
+++ b/pandas/core/api.py
@@ -51,16 +51,13 @@
 from pandas.core.indexes.api import (
     CategoricalIndex,
     DatetimeIndex,
-    Float64Index,
     Index,
-    Int64Index,
     IntervalIndex,
     MultiIndex,
     NumericIndex,
     PeriodIndex,
     RangeIndex,
     TimedeltaIndex,
-    UInt64Index,
 )
 from pandas.core.indexes.datetimes import (
     bdate_range,
@@ -101,14 +98,12 @@
     "Flags",
     "Float32Dtype",
     "Float64Dtype",
-    "Float64Index",
     "Grouper",
     "Index",
     "IndexSlice",
     "Int16Dtype",
     "Int32Dtype",
     "Int64Dtype",
-    "Int64Index",
     "Int8Dtype",
     "Interval",
     "IntervalDtype",
@@ -141,7 +136,6 @@
     "UInt16Dtype",
     "UInt32Dtype",
     "UInt64Dtype",
-    "UInt64Index",
     "UInt8Dtype",
     "unique",
     "value_counts",
diff --git a/pandas/core/arrays/timedeltas.py b/pandas/core/arrays/timedeltas.py
index 1a2b9728f80a1..50203003a037a 100644
--- a/pandas/core/arrays/timedeltas.py
+++ b/pandas/core/arrays/timedeltas.py
@@ -726,10 +726,10 @@ def total_seconds(self) -> npt.NDArray[np.float64]:
 
         Returns
         -------
-        ndarray, Float64Index or Series
+        ndarray, Index or Series
             When the calling object is a TimedeltaArray, the return type
             is ndarray.  When the calling object is a TimedeltaIndex,
-            the return type is a Float64Index. When the calling object
+            the return type is an Index with a float64 dtype. When the calling object
             is a Series, the return type is Series of type `float64` whose
             index is the same as the original.
 
diff --git a/pandas/core/frame.py b/pandas/core/frame.py
index 3b122eaa814e5..9efc07628cccd 100644
--- a/pandas/core/frame.py
+++ b/pandas/core/frame.py
@@ -10870,11 +10870,7 @@ def quantile(
                 f"Invalid method: {method}. Method must be in {valid_method}."
             )
         if method == "single":
-            # error: Argument "qs" to "quantile" of "BlockManager" has incompatible type
-            # "Index"; expected "Float64Index"
-            res = data._mgr.quantile(
-                qs=q, axis=1, interpolation=interpolation  # type: ignore[arg-type]
-            )
+            res = data._mgr.quantile(qs=q, axis=1, interpolation=interpolation)
         elif method == "table":
             valid_interpolation = {"nearest", "lower", "higher"}
             if interpolation not in valid_interpolation:
diff --git a/pandas/core/groupby/grouper.py b/pandas/core/groupby/grouper.py
index e0e5c15f6adfc..ffc886711932b 100644
--- a/pandas/core/groupby/grouper.py
+++ b/pandas/core/groupby/grouper.py
@@ -880,7 +880,7 @@ def is_in_axis(key) -> bool:
             try:
                 items.get_loc(key)
             except (KeyError, TypeError, InvalidIndexError):
-                # TypeError shows up here if we pass e.g. Int64Index
+                # TypeError shows up here if we pass e.g. an Index
                 return False
 
         return True
diff --git a/pandas/core/indexes/api.py b/pandas/core/indexes/api.py
index c562eaffd241d..08191363bfc93 100644
--- a/pandas/core/indexes/api.py
+++ b/pandas/core/indexes/api.py
@@ -26,12 +26,7 @@
 from pandas.core.indexes.datetimes import DatetimeIndex
 from pandas.core.indexes.interval import IntervalIndex
 from pandas.core.indexes.multi import MultiIndex
-from pandas.core.indexes.numeric import (
-    Float64Index,
-    Int64Index,
-    NumericIndex,
-    UInt64Index,
-)
+from pandas.core.indexes.numeric import NumericIndex
 from pandas.core.indexes.period import PeriodIndex
 from pandas.core.indexes.range import RangeIndex
 from pandas.core.indexes.timedeltas import TimedeltaIndex
@@ -52,12 +47,9 @@
     "Index",
     "MultiIndex",
     "NumericIndex",
-    "Float64Index",
-    "Int64Index",
     "CategoricalIndex",
     "IntervalIndex",
     "RangeIndex",
-    "UInt64Index",
     "InvalidIndexError",
     "TimedeltaIndex",
     "PeriodIndex",
diff --git a/pandas/core/indexes/base.py b/pandas/core/indexes/base.py
index 4d2e4758817be..fc2c51166a737 100644
--- a/pandas/core/indexes/base.py
+++ b/pandas/core/indexes/base.py
@@ -296,9 +296,6 @@ class Index(IndexOpsMixin, PandasObject):
     TimedeltaIndex : Index of timedelta64 data.
     PeriodIndex : Index of Period data.
     NumericIndex : Index of numpy int/uint/float data.
-    Int64Index : Index of purely int64 labels (deprecated).
-    UInt64Index : Index of purely uint64 labels (deprecated).
-    Float64Index : Index of  purely float64 labels (deprecated).
 
     Notes
     -----
@@ -498,7 +495,7 @@ def __new__(
 
         klass = cls._dtype_to_subclass(arr.dtype)
 
-        # _ensure_array _may_ be unnecessary once Int64Index etc are gone
+        # _ensure_array _may_ be unnecessary once NumericIndex etc are gone
         arr = klass._ensure_array(arr, arr.dtype, copy=False)
         return klass._simple_new(arr, name)
 
@@ -1026,7 +1023,7 @@ def take(
             taken = values.take(
                 indices, allow_fill=allow_fill, fill_value=self._na_value
             )
-        # _constructor so RangeIndex->Int64Index
+        # _constructor so RangeIndex-> Index with an int64 dtype
         return self._constructor._simple_new(taken, name=self.name)
 
     @final
@@ -1097,7 +1094,7 @@ def repeat(self, repeats, axis=None):
         nv.validate_repeat((), {"axis": axis})
         res_values = self._values.repeat(repeats)
 
-        # _constructor so RangeIndex->Int64Index
+        # _constructor so RangeIndex-> Index with an int64 dtype
         return self._constructor._simple_new(res_values, name=self.name)
 
     # --------------------------------------------------------------------
@@ -6228,7 +6225,7 @@ def _maybe_cast_slice_bound(self, label, side: str_t):
         """
 
         # We are a plain index here (sub-class override this method if they
-        # wish to have special treatment for floats/ints, e.g. Float64Index and
+        # wish to have special treatment for floats/ints, e.g. NumericIndex and
         # datetimelike Indexes
         # Special case numeric EA Indexes, since they are not handled by NumericIndex
 
@@ -6442,7 +6439,7 @@ def delete(self: _IndexT, loc) -> _IndexT:
         else:
             res_values = values.delete(loc)
 
-        # _constructor so RangeIndex->Int64Index
+        # _constructor so RangeIndex-> Index with an int64 dtype
         return self._constructor._simple_new(res_values, name=self.name)
 
     def insert(self, loc: int, item) -> Index:
diff --git a/pandas/core/indexes/datetimes.py b/pandas/core/indexes/datetimes.py
index bdaaeb20b3508..fc40ae4a99be0 100644
--- a/pandas/core/indexes/datetimes.py
+++ b/pandas/core/indexes/datetimes.py
@@ -63,7 +63,7 @@
 if TYPE_CHECKING:
     from pandas.core.api import (
         DataFrame,
-        Float64Index,
+        NumericIndex,
         PeriodIndex,
     )
 
@@ -284,11 +284,11 @@ def to_period(self, freq=None) -> PeriodIndex:
         return PeriodIndex._simple_new(arr, name=self.name)
 
     @doc(DatetimeArray.to_julian_date)
-    def to_julian_date(self) -> Float64Index:
-        from pandas.core.indexes.api import Float64Index
+    def to_julian_date(self) -> NumericIndex:
+        from pandas.core.indexes.api import NumericIndex
 
         arr = self._data.to_julian_date()
-        return Float64Index._simple_new(arr, name=self.name)
+        return NumericIndex._simple_new(arr, name=self.name)
 
     @doc(DatetimeArray.isocalendar)
     def isocalendar(self) -> DataFrame:
diff --git a/pandas/core/indexes/interval.py b/pandas/core/indexes/interval.py
index c6bd7b8aae980..3b0b5d1d55a88 100644
--- a/pandas/core/indexes/interval.py
+++ b/pandas/core/indexes/interval.py
@@ -520,7 +520,7 @@ def _maybe_convert_i8(self, key):
         -------
         scalar or list-like
             The original key if no conversion occurred, int if converted scalar,
-            Int64Index if converted list-like.
+            Index with an int64 dtype if converted list-like.
         """
         if is_list_like(key):
             key = ensure_index(key)
diff --git a/pandas/core/indexes/multi.py b/pandas/core/indexes/multi.py
index d7fba354ceead..14240f1d19472 100644
--- a/pandas/core/indexes/multi.py
+++ b/pandas/core/indexes/multi.py
@@ -1642,7 +1642,7 @@ def get_level_values(self, level):
         level_1    int64
         dtype: object
         >>> pd.MultiIndex.from_arrays([[1, None, 2], [3, 4, 5]]).get_level_values(0)
-        Float64Index([1.0, nan, 2.0], dtype='float64')
+        NumericIndex([1.0, nan, 2.0], dtype='float64')
         """
         level = self._get_level_number(level)
         values = self._get_level_values(level)
diff --git a/pandas/core/indexes/numeric.py b/pandas/core/indexes/numeric.py
index 3ea7b30f7e9f1..fab741ec1ba18 100644
--- a/pandas/core/indexes/numeric.py
+++ b/pandas/core/indexes/numeric.py
@@ -1,16 +1,10 @@
 from __future__ import annotations
 
-from typing import (
-    Callable,
-    Hashable,
-)
+from typing import Callable
 
 import numpy as np
 
-from pandas._libs import (
-    index as libindex,
-    lib,
-)
+from pandas._libs import index as libindex
 from pandas._typing import (
     Dtype,
     npt,
@@ -26,8 +20,6 @@
     is_integer_dtype,
     is_numeric_dtype,
     is_scalar,
-    is_signed_integer_dtype,
-    is_unsigned_integer_dtype,
     pandas_dtype,
 )
 from pandas.core.dtypes.generic import ABCSeries
@@ -68,9 +60,6 @@ class NumericIndex(Index):
     See Also
     --------
     Index : The base pandas Index type.
-    Int64Index : Index of purely int64 labels (deprecated).
-    UInt64Index : Index of purely uint64 labels (deprecated).
-    Float64Index : Index of  purely float64 labels (deprecated).
 
     Notes
     -----
@@ -146,18 +135,12 @@ def _ensure_array(cls, data, dtype, copy: bool):
             if not isinstance(data, (ABCSeries, list, tuple)):
                 data = list(data)
 
-            orig = data
             if isinstance(data, (list, tuple)):
                 if len(data):
                     data = sanitize_array(data, index=None)
                 else:
                     data = np.array([], dtype=np.int64)
 
-            if dtype is None and data.dtype.kind == "f":
-                if cls is UInt64Index and (data >= 0).all():
-                    # https://github.com/numpy/numpy/issues/19146
-                    data = np.asarray(orig, dtype=np.uint64)
-
         dtype = cls._ensure_dtype(dtype)
 
         if copy or not is_dtype_equal(data.dtype, dtype):
@@ -199,8 +182,6 @@ def _validate_dtype(cls, dtype: Dtype | None) -> None:
     @classmethod
     def _ensure_dtype(cls, dtype: Dtype | None) -> np.dtype | None:
         """
-        Ensure int64 dtype for Int64Index etc. but allow int32 etc. for NumericIndex.
-
         Assumes dtype has already been validated.
         """
         if dtype is None:
@@ -243,14 +224,6 @@ def _maybe_cast_slice_bound(self, label, side: str):
 
     # ----------------------------------------------------------------
 
-    @doc(Index._shallow_copy)
-    def _shallow_copy(self, values, name: Hashable = lib.no_default):
-        if not self._can_hold_na and values.dtype.kind == "f":
-            name = self._name if name is lib.no_default else name
-            # Ensure we are not returning an Int64Index with float data:
-            return Float64Index._simple_new(values, name=name)
-        return super()._shallow_copy(values=values, name=name)
-
     def _convert_tolerance(self, tolerance, target):
         tolerance = super()._convert_tolerance(tolerance, target)
 
@@ -308,108 +281,3 @@ def _format_native_types(
             quoting=quoting,
             **kwargs,
         )
-
-
-_num_index_shared_docs = {}
-
-
-_num_index_shared_docs[
-    "class_descr"
-] = """
-    Immutable sequence used for indexing and alignment.
-
-    .. deprecated:: 1.4.0
-        In pandas v2.0 %(klass)s will be removed and :class:`NumericIndex` used instead.
-        %(klass)s will remain fully functional for the duration of pandas 1.x.
-
-    The basic object storing axis labels for all pandas objects.
-    %(klass)s is a special case of `Index` with purely %(ltype)s labels. %(extra)s.
-
-    Parameters
-    ----------
-    data : array-like (1-dimensional)
-    dtype : NumPy dtype (default: %(dtype)s)
-    copy : bool
-        Make a copy of input ndarray.
-    name : object
-        Name to be stored in the index.
-
-    Attributes
-    ----------
-    None
-
-    Methods
-    -------
-    None
-
-    See Also
-    --------
-    Index : The base pandas Index type.
-    NumericIndex : Index of numpy int/uint/float data.
-
-    Notes
-    -----
-    An Index instance can **only** contain hashable objects.
-"""
-
-
-class IntegerIndex(NumericIndex):
-    """
-    This is an abstract class for Int64Index, UInt64Index.
-    """
-
-    _is_backward_compat_public_numeric_index: bool = False
-
-
-class Int64Index(IntegerIndex):
-    _index_descr_args = {
-        "klass": "Int64Index",
-        "ltype": "integer",
-        "dtype": "int64",
-        "extra": "",
-    }
-    __doc__ = _num_index_shared_docs["class_descr"] % _index_descr_args
-
-    _typ = "int64index"
-    _default_dtype = np.dtype(np.int64)
-    _dtype_validation_metadata = (is_signed_integer_dtype, "signed integer")
-
-    @property
-    def _engine_type(self) -> type[libindex.Int64Engine]:
-        return libindex.Int64Engine
-
-
-class UInt64Index(IntegerIndex):
-    _index_descr_args = {
-        "klass": "UInt64Index",
-        "ltype": "unsigned integer",
-        "dtype": "uint64",
-        "extra": "",
-    }
-    __doc__ = _num_index_shared_docs["class_descr"] % _index_descr_args
-
-    _typ = "uint64index"
-    _default_dtype = np.dtype(np.uint64)
-    _dtype_validation_metadata = (is_unsigned_integer_dtype, "unsigned integer")
-
-    @property
-    def _engine_type(self) -> type[libindex.UInt64Engine]:
-        return libindex.UInt64Engine
-
-
-class Float64Index(NumericIndex):
-    _index_descr_args = {
-        "klass": "Float64Index",
-        "dtype": "float64",
-        "ltype": "float",
-        "extra": "",
-    }
-    __doc__ = _num_index_shared_docs["class_descr"] % _index_descr_args
-
-    _typ = "float64index"
-    _default_dtype = np.dtype(np.float64)
-    _dtype_validation_metadata = (is_float_dtype, "float")
-
-    @property
-    def _engine_type(self) -> type[libindex.Float64Engine]:
-        return libindex.Float64Engine
diff --git a/pandas/core/indexes/period.py b/pandas/core/indexes/period.py
index 877bb2844e8c9..b32f0954a7c30 100644
--- a/pandas/core/indexes/period.py
+++ b/pandas/core/indexes/period.py
@@ -45,7 +45,6 @@
     Index,
 )
 from pandas.core.indexes.extension import inherit_names
-from pandas.core.indexes.numeric import Int64Index
 
 _index_doc_kwargs = dict(ibase._index_doc_kwargs)
 _index_doc_kwargs.update({"target_klass": "PeriodIndex or list of Periods"})
@@ -183,18 +182,18 @@ def to_timestamp(self, freq=None, how: str = "start") -> DatetimeIndex:
 
     @property
     @doc(PeriodArray.hour.fget)
-    def hour(self) -> Int64Index:
-        return Int64Index(self._data.hour, name=self.name)
+    def hour(self) -> Index:
+        return Index(self._data.hour, name=self.name)
 
     @property
     @doc(PeriodArray.minute.fget)
-    def minute(self) -> Int64Index:
-        return Int64Index(self._data.minute, name=self.name)
+    def minute(self) -> Index:
+        return Index(self._data.minute, name=self.name)
 
     @property
     @doc(PeriodArray.second.fget)
-    def second(self) -> Int64Index:
-        return Int64Index(self._data.second, name=self.name)
+    def second(self) -> Index:
+        return Index(self._data.second, name=self.name)
 
     # ------------------------------------------------------------------------
     # Index Constructors
diff --git a/pandas/core/indexing.py b/pandas/core/indexing.py
index 636e376197ef1..a7b19e3180fff 100644
--- a/pandas/core/indexing.py
+++ b/pandas/core/indexing.py
@@ -2081,7 +2081,7 @@ def _setitem_with_indexer_missing(self, indexer, value):
             new_index = index.insert(len(index), indexer)
 
             # we have a coerced indexer, e.g. a float
-            # that matches in an Int64Index, so
+            # that matches in an int64 Index, so
             # we will not create a duplicate index, rather
             # index to that element
             # e.g. 0.0 -> 0
diff --git a/pandas/core/internals/array_manager.py b/pandas/core/internals/array_manager.py
index b8ef925362e7b..82316806d3d47 100644
--- a/pandas/core/internals/array_manager.py
+++ b/pandas/core/internals/array_manager.py
@@ -4,7 +4,6 @@
 from __future__ import annotations
 
 from typing import (
-    TYPE_CHECKING,
     Any,
     Callable,
     Hashable,
@@ -94,10 +93,6 @@
     to_native_types,
 )
 
-if TYPE_CHECKING:
-    from pandas.core.api import Float64Index
-
-
 T = TypeVar("T", bound="BaseArrayManager")
 
 
@@ -1013,7 +1008,7 @@ def operate_blockwise(self, other: ArrayManager, array_op) -> ArrayManager:
     def quantile(
         self,
         *,
-        qs: Float64Index,
+        qs: Index,  # with dtype float64
         axis: AxisInt = 0,
         transposed: bool = False,
         interpolation: QuantileInterpolation = "linear",
diff --git a/pandas/core/internals/blocks.py b/pandas/core/internals/blocks.py
index 4bb4882574228..00ab9d02cee00 100644
--- a/pandas/core/internals/blocks.py
+++ b/pandas/core/internals/blocks.py
@@ -1288,8 +1288,8 @@ def quantile(
 
         Parameters
         ----------
-        qs : Float64Index
-            List of the quantiles to be computed.
+        qs : Index
+            The quantiles to be computed in float64.
         interpolation : str, default 'linear'
             Type of interpolation.
         axis : int, default 0