diff --git a/onnxruntime/python/tools/symbolic_shape_infer.py b/onnxruntime/python/tools/symbolic_shape_infer.py
index d194b5d55a..11e87edec2 100755
--- a/onnxruntime/python/tools/symbolic_shape_infer.py
+++ b/onnxruntime/python/tools/symbolic_shape_infer.py
@@ -1142,7 +1142,44 @@ class SymbolicShapeInference:
     def _infer_SplitToSequence(self, node):
         self._infer_Split_Common(node, helper.make_sequence_value_info)
 
-    def _infer_Squeeze(self, node):
+    def _infer_Squeeze(self, node): 
+        input_shape = self._get_shape(node, 0)
+        op_set = get_opset(self.out_mp_)
+
+        # Depending on op-version 'axes' are provided as attribute or via 2nd input
+        if op_set < 13:    
+            axes = get_attribute(node, 'axes')
+            assert self._try_get_value(node, 1) is None
+        else:
+            axes = self._try_get_value(node, 1)
+            assert get_attribute(node, 'axes') is None
+
+        if axes is None:
+            # No axes have been provided (neither via attribute nor via input).
+            # In this case the 'Shape' op should remove all axis with dimension 1.
+            # For symbolic dimensions we guess they are !=1.
+            output_shape = [s for s in input_shape if s != 1]
+            if self.verbose_ > 0:
+                symbolic_dimensions = [s for s in input_shape if type(s) != int]
+                if len(symbolic_dimensions) > 0:
+                    print(f"Symbolic dimensions in input shape of op: '{node.op_type}' node: '{node.name}'. " +
+                          f"Assuming the following dimensions are never equal to 1: {symbolic_dimensions}")
+        else:
+            axes = [handle_negative_axis(a, len(input_shape)) for a in axes]
+            output_shape = []
+            for i in range(len(input_shape)):
+                if i not in axes:
+                    output_shape.append(input_shape[i])
+                else:
+                    assert input_shape[i] == 1 or type(input_shape[i]) != int
+                    if self.verbose_ > 0 and type(input_shape[i]) != int:
+                        print(f"Symbolic dimensions in input shape of op: '{node.op_type}' node: '{node.name}'. " +
+                              f"Assuming the dimension '{input_shape[i]}' at index {i} of the input to be equal to 1.")
+
+        vi = self.known_vi_[node.output[0]]
+        vi.CopyFrom(
+            helper.make_tensor_value_info(node.output[0], self.known_vi_[node.input[0]].type.tensor_type.elem_type,
+                                          output_shape))
         self._pass_on_sympy_data(node)
 
     def _infer_Tile(self, node):