diff --git a/python/tvm/relay/frontend/onnx.py b/python/tvm/relay/frontend/onnx.py
index eb7fa00d6f3e..16686963d3be 100644
--- a/python/tvm/relay/frontend/onnx.py
+++ b/python/tvm/relay/frontend/onnx.py
@@ -3707,6 +3707,59 @@ def _impl_v1(cls, inputs, attr, params):
         return _expr.TupleWrapper(_expr.Tuple(result), len(result))
 
 
+class Momentum(OnnxOpConverter):
+    """Operator converter for Momentum op."""
+
+    @classmethod
+    def _impl_v1(cls, inputs, attr, params):
+        alpha = attr["alpha"]
+        beta = attr["beta"]
+        mode = attr["mode"].decode("utf-8")
+        norm_coefficient = attr["norm_coefficient"]
+
+        assert mode in ["nesterov", "standard"], f"Unknown momentum mode {mode}"
+        R = inputs[0]
+        T = inputs[1]
+
+        assert (
+            len(inputs) - 2
+        ) % 3 == 0, f"Expect triplets for remaining inputs, found {len(inputs) - 2}"
+        # Remaining inputs are:
+        # [x_1, x_2 ..., x_1_gradient, x_2_gradient, ... x_1_momentum, x_2_momentum...]
+        num_input_tensors = (len(inputs) - 2) // 3
+
+        # convert attributes to constants
+        dtype_inputs = infer_type(inputs[3]).checked_type.dtype
+        alpha = relay.const(alpha, dtype=dtype_inputs)
+        beta = relay.const(beta, dtype=dtype_inputs)
+        norm_coefficient = relay.const(norm_coefficient, dtype=dtype_inputs)
+        default_beta = relay.const(1.0, dtype=dtype_inputs)
+
+        # Calculate updated values for every input
+        output_tensors = []
+        output_momentums = []
+        for i in range(num_input_tensors):
+            x = inputs[i + 2]
+            gradient = inputs[i + 2 + num_input_tensors]
+            momentum = inputs[i + 2 + 2 * num_input_tensors]
+            g_regularized = norm_coefficient * x + gradient
+            beta_adjusted = relay.If(T > relay.const(0, dtype="int64"), beta, default_beta)
+            new_momentum = alpha * momentum + beta_adjusted * g_regularized
+
+            if mode == "standard":
+                x_output = x - R * new_momentum
+            else:
+                # mode == 'nesterov'
+                x_output = x - R * (g_regularized + alpha * new_momentum)
+
+            output_tensors.append(x_output)
+            output_momentums.append(new_momentum)
+
+        # append lists together, momentums come after result tensors
+        result = output_tensors + output_momentums
+        return _expr.TupleWrapper(_expr.Tuple(result), len(result))
+
+
 # compatible operators that do NOT require any conversion.
 _identity_list = []
 
@@ -3895,6 +3948,7 @@ def _get_convert_map(opset):
         "NegativeLogLikelihoodLoss": NegativeLogLikelihoodLoss.get_converter(opset),
         "Adagrad": Adagrad.get_converter(opset),
         "Adam": Adam.get_converter(opset),
+        "Momentum": Momentum.get_converter(opset),
     }
 
 
diff --git a/tests/python/frontend/onnx/test_forward.py b/tests/python/frontend/onnx/test_forward.py
index ecef39ada53f..056d1b8fffdf 100644
--- a/tests/python/frontend/onnx/test_forward.py
+++ b/tests/python/frontend/onnx/test_forward.py
@@ -4755,10 +4755,7 @@ def verify_eyelike(indata):
     "test_maxpool_with_argmax_2d_precomputed_pads",
     "test_maxpool_with_argmax_2d_precomputed_strides",
     "test_maxunpool_export_with_output_shape",
-    "test_momentum",
-    "test_momentum_multiple",
     "test_mvn",
-    "test_nesterov_momentum",
     # When unsqueeze is fully supported, remaining nllloss tests should work:
     "test_nllloss_NC_expanded",
     "test_nllloss_NCd1_expanded",