diff --git a/CHANGELOG.md b/CHANGELOG.md
index 9fcd0e8f1cf..b886a933fb8 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -30,7 +30,10 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - Added new detection metric `PanopticQuality` ([#929](https://github.com/PyTorchLightning/metrics/pull/929))
 
 
-- Add `ClassificationTask` Enum and use in metrics ([#1479](https://github.com/Lightning-AI/metrics/pull/1479))
+- Added `ClassificationTask` Enum and use in metrics ([#1479](https://github.com/Lightning-AI/metrics/pull/1479))
+
+
+- Added `ignore_index` option to `exact_match` metric ([#1540](https://github.com/Lightning-AI/metrics/pull/1540))
 
 
 ### Changed
diff --git a/src/torchmetrics/classification/exact_match.py b/src/torchmetrics/classification/exact_match.py
index 0ad8f036096..745208fce9f 100644
--- a/src/torchmetrics/classification/exact_match.py
+++ b/src/torchmetrics/classification/exact_match.py
@@ -124,7 +124,8 @@ def update(self, preds, target) -> None:
                 preds, target, self.num_classes, self.multidim_average, self.ignore_index
             )
         preds, target = _multiclass_stat_scores_format(preds, target, 1)
-        correct, total = _multiclass_exact_match_update(preds, target, self.multidim_average)
+
+        correct, total = _multiclass_exact_match_update(preds, target, self.multidim_average, self.ignore_index)
         if self.multidim_average == "samplewise":
             self.correct.append(correct)
             self.total = total
diff --git a/src/torchmetrics/functional/classification/exact_match.py b/src/torchmetrics/functional/classification/exact_match.py
index da41cb8c016..43f39932617 100644
--- a/src/torchmetrics/functional/classification/exact_match.py
+++ b/src/torchmetrics/functional/classification/exact_match.py
@@ -41,8 +41,13 @@ def _multiclass_exact_match_update(
     preds: Tensor,
     target: Tensor,
     multidim_average: Literal["global", "samplewise"] = "global",
+    ignore_index: Optional[int] = None,
 ) -> Tuple[Tensor, Tensor]:
     """Compute the statistics."""
+    if ignore_index is not None:
+        preds = preds.clone()
+        preds[target == ignore_index] = ignore_index
+
     correct = (preds == target).sum(1) == preds.shape[1]
     correct = correct if multidim_average == "samplewise" else correct.sum()
     total = torch.tensor(preds.shape[0] if multidim_average == "global" else 1, device=correct.device)
@@ -109,7 +114,7 @@ def multiclass_exact_match(
         _multiclass_stat_scores_arg_validation(num_classes, top_k, average, multidim_average, ignore_index)
         _multiclass_stat_scores_tensor_validation(preds, target, num_classes, multidim_average, ignore_index)
     preds, target = _multiclass_stat_scores_format(preds, target, top_k)
-    correct, total = _multiclass_exact_match_update(preds, target, multidim_average)
+    correct, total = _multiclass_exact_match_update(preds, target, multidim_average, ignore_index)
     return _exact_match_reduce(correct, total)
 
 
diff --git a/tests/unittests/classification/test_exact_match.py b/tests/unittests/classification/test_exact_match.py
index 73eb4cf738a..32f5a2aedd1 100644
--- a/tests/unittests/classification/test_exact_match.py
+++ b/tests/unittests/classification/test_exact_match.py
@@ -34,8 +34,8 @@ def _baseline_exact_match_multiclass(preds, target, ignore_index, multidim_avera
     target = target.numpy()
 
     if ignore_index is not None:
-        target = np.copy(target)
-        target[target == ignore_index] = -1
+        preds = np.copy(preds)
+        preds[target == ignore_index] = ignore_index
 
     correct = (preds == target).sum(-1) == preds.shape[1]
     correct = correct.sum() if multidim_average == "global" else correct