diff --git a/python/tvm/relax/frontend/onnx/onnx_frontend.py b/python/tvm/relax/frontend/onnx/onnx_frontend.py
index dd4b8a425425..799a32e405f4 100644
--- a/python/tvm/relax/frontend/onnx/onnx_frontend.py
+++ b/python/tvm/relax/frontend/onnx/onnx_frontend.py
@@ -1917,18 +1917,20 @@ def _impl_v13(cls, bb, inputs, attr, params):
         # If possible, directly expand to constant shape.
         if isinstance(shape, relax.Constant):
             new_shape = shape.data.numpy().tolist()
-            # For some reason, onnx allows target shapes to be smaller than input shapes.
-            # We need to go correct it.
+            # ONNX Expand operator requires preserving target rank and broadcasting
+            # according to standard rules. Dimensions are right-aligned.
             data_shape = [dim.value for dim in data.struct_info.shape]
-            # Dimensions are right alignment.
-            data_shape = [1] * (len(new_shape) - len(data_shape)) + data_shape
-            # Fix small target shapes.
-            for i, s in enumerate(new_shape):
-                if i < len(data_shape) and s < data_shape[i]:
+            
+            # Right-align the shapes
+            if len(new_shape) > len(data_shape):
+                data_shape = [1] * (len(new_shape) - len(data_shape)) + data_shape
+            else:
+                new_shape = [1] * (len(data_shape) - len(new_shape)) + new_shape
+            # Fix small target shapes - if target dim is smaller than input dim
+            # use the input dim (ONNX-specific behavior).
+            for i in range(len(new_shape)):
+                if new_shape[i] < data_shape[i]:
                     new_shape[i] = data_shape[i]
-            # If the new shape matches the input shape, no transformation is needed.
-            if new_shape == data_shape:
-                return data
             return relax.op.broadcast_to(data, relax.ShapeExpr(new_shape))
 
         # Otherwise handle dynamic shapes.