diff --git a/src/frontends/pytorch/src/op/cat.cpp b/src/frontends/pytorch/src/op/cat.cpp
index 08f828e9d64385..c0ee57854aeb9b 100644
--- a/src/frontends/pytorch/src/op/cat.cpp
+++ b/src/frontends/pytorch/src/op/cat.cpp
@@ -104,22 +104,20 @@ OutputVector translate_quantized_cat(const NodeContext& context) {
 OutputVector translate_stack_fx(const NodeContext& context) {
     num_inputs_check(context, 1, context.get_input_size());
     auto dim = context.mark_node(v0::Constant::create(element::i32, Shape{}, {0}));
+    int64_t axis = 0;
+
     std::deque<Output<Node>> list_elems;
     auto num_elements = context.get_input_size();
-    for (size_t i = 0; i < num_elements - 1; i++) {
-        if (context.get_input(i).get_partial_shape().rank() == 1)
-            dim = context.mark_node(v0::Constant::create(element::i32, Shape{}, {1}));
-        auto stack_input =
-            context.mark_node(std::make_shared<v0::Unsqueeze>(context.get_input(static_cast<int>(i)), dim));
-        list_elems.push_back(stack_input);
-    }
-    int64_t axis = 0;
+
     if (!context.get_input_type(num_elements - 1).is<type::List>()) {
-        // axis can be not present and that means that last input will have List type
         axis = context.const_input<int64_t>(num_elements - 1);
-    } else {
-        auto stack_input = context.mark_node(
-            std::make_shared<v0::Unsqueeze>(context.get_input(static_cast<int>(num_elements - 1)), dim));
+        dim = context.mark_node(v0::Constant::create(element::i32, Shape{}, {axis}));
+        num_elements -= 1;
+    }
+
+    for (size_t i = 0; i < num_elements; i++) {
+        auto stack_input =
+            context.mark_node(std::make_shared<v0::Unsqueeze>(context.get_input(static_cast<int>(i)), dim));
         list_elems.push_back(stack_input);
     }
     return translate_cat_common(context, list_elems, axis, true);