diff --git a/coremltools/converters/mil/mil/ops/defs/iOS15/elementwise_unary.py b/coremltools/converters/mil/mil/ops/defs/iOS15/elementwise_unary.py
index 4122a757b..c4ab0ccc6 100644
--- a/coremltools/converters/mil/mil/ops/defs/iOS15/elementwise_unary.py
+++ b/coremltools/converters/mil/mil/ops/defs/iOS15/elementwise_unary.py
@@ -446,7 +446,18 @@ def type_inference(self):
 
     @precondition(allow=VALUE)
     def value_inference(self):
-        return np.asarray(np.reciprocal(self.x.val + self.epsilon.val))
+        x_val = self.x.val
+        epsilon_val = self.epsilon.val
+
+        # ✅ New check for unsupported int types
+        if np.issubdtype(x_val.dtype, np.integer):
+            raise ValueError(
+                f"CoreML 'inverse' op does not support integer types. "
+                f"Got input dtype: {x_val.dtype}. Please cast input to float32 or use float constants."
+            )
+
+        return np.asarray(np.reciprocal(x_val + epsilon_val))
+
 
 
 @register_op
diff --git a/coremltools/test/test_inverse_conversion.py b/coremltools/test/test_inverse_conversion.py
new file mode 100644
index 000000000..feb249991
--- /dev/null
+++ b/coremltools/test/test_inverse_conversion.py
@@ -0,0 +1,22 @@
+import torch
+import torch.nn as nn
+import coremltools as ct
+
+def test_inverse_with_int32_shape_input():
+    class Model(nn.Module):
+        def forward(self, x):
+            return 16 / x.shape[0]  # int32 division
+
+    model = Model()
+    model.eval()  # Make sure to silence the eval warning
+    inputs = (torch.randn(2, 3, 4),)
+    traced = torch.jit.trace(model, inputs)
+
+    try:
+        ct.convert(traced, inputs=[ct.TensorType(shape=(2, 3, 4))], convert_to="mlprogram")
+    except ValueError as e:
+        error_str = str(e).lower()
+        assert "inverse" in error_str
+        assert any(keyword in error_str for keyword in ["int32", "integer"])
+    else:
+        assert False, "Expected ValueError due to int32 input to inverse op"