diff --git a/python/mxnet/contrib/onnx/mx2onnx/_op_translations.py b/python/mxnet/contrib/onnx/mx2onnx/_op_translations.py
index 59082c961475..dd81b063252d 100644
--- a/python/mxnet/contrib/onnx/mx2onnx/_op_translations.py
+++ b/python/mxnet/contrib/onnx/mx2onnx/_op_translations.py
@@ -3177,3 +3177,44 @@ def convert_reshape_like(node, **kwargs):
     ]
 
     return nodes
+
+
+@mx_op.register("gather_nd")
+def convert_gather_nd(node, **kwargs):
+    """Map MXNet's gather_ND operator attributes to onnx's operator.
+    """
+    from onnx.helper import make_node
+    name, input_nodes, _ = get_inputs(node, kwargs)
+
+    data = input_nodes[0]
+    indices = input_nodes[1]
+
+    # Onnx Transpose operator takes perm as a parameter, so we need to 'pad'
+    # the input to a known dim (10 here)
+    perm = [9] + [i for i in range(1, 9)] + [0]
+
+    nodes = [
+        create_tensor([0], name+'_0', kwargs['initializer']),
+        create_tensor([1], name+'_1', kwargs['initializer']),
+        create_tensor([10], name+'_10', kwargs['initializer']),
+        # Generate 10-d filter
+        make_node('Shape', [indices], [name+'_indices_shape']),
+        make_node('Shape', [name+'_indices_shape'], [name+'_indices_dim']),
+        make_node('Sub', [name+'_10', name+'_indices_dim'], [name+'_sub0_out']),
+        make_node('Concat', [name+'_0', name+'_sub0_out'], [name+'_concat0_out'], axis=0),
+        make_node('Pad', [name+'_indices_shape', name+'_concat0_out', name+'_1'], [name+'_shape_10_dim']),
+        make_node('Reshape', [indices, name+'_shape_10_dim'], [name+'_indices_10_dim']),
+        make_node('Transpose', [name+'_indices_10_dim'], [name+'_transpose0_output'], perm=perm),
+        # Reshape filter to acutall dim for GatherND computation
+        make_node('Slice', [name+'_indices_shape', name+'_0', name+'_1'],
+                  [name+'_slice0_out']),
+        make_node('Slice', [name+'_indices_shape', name+'_1', name+'_indices_dim'],
+                  [name+'_slice1_out']),
+        make_node('Concat', [name+'_slice1_out', name+'_slice0_out'], [name+'_concat1_out'], axis=0),
+        make_node('Reshape', [name+'_transpose0_output', name+'_concat1_out'], [name+'_reshape0_out']),
+        # Cast data type for indicies
+        make_node('Cast', [name+'_reshape0_out'], [name+'_cast0_out'], to=int(onnx.TensorProto.INT64)),
+        make_node('GatherND', [data, name+'_cast0_out'], [name], name=name)
+    ]
+
+    return nodes
diff --git a/tests/python-pytest/onnx/test_operators.py b/tests/python-pytest/onnx/test_operators.py
index 1c4629d6f871..9b0921be7f8b 100644
--- a/tests/python-pytest/onnx/test_operators.py
+++ b/tests/python-pytest/onnx/test_operators.py
@@ -463,3 +463,17 @@ def test_onnx_export_reshape_like(tmp_path, dtype):
     M4 = def_model('reshape_like', lhs_begin=0, lhs_end=None, rhs_begin=1, rhs_end=None)
     op_export_test('reshape_like4', M4, [x, y], tmp_path)
 
+
+@pytest.mark.parametrize('dtype', ['int32', 'int64', 'float16', 'float32', 'float64'])
+def test_onnx_export_gather_nd(tmp_path, dtype):
+    # y[0] == dim(x)
+    x1 = mx.random.uniform(-100, 100, (4, 5, 6, 7)).astype(dtype)
+    y1 = mx.random.randint(-4, 4, (4, 4, 4)).astype(dtype)
+    M1 = def_model('gather_nd')
+    op_export_test('gather_nd1', M1, [x1, y1], tmp_path)
+    # y[0] < dim(x)
+    x2 = mx.random.uniform(-100, 100, (4, 5, 6, 7)).astype(dtype)
+    y2 = mx.random.randint(-4, 4, (2,3,4)).astype(dtype)
+    M2 = def_model('gather_nd')
+    op_export_test('gather_nd2', M2, [x2, y2], tmp_path)
+