diff --git a/src/frontends/onnx/frontend/src/op/softmax_crossentropy_loss.cpp b/src/frontends/onnx/frontend/src/op/softmax_crossentropy_loss.cpp
index 4e2573debb187d..dfa97577361fb0 100644
--- a/src/frontends/onnx/frontend/src/op/softmax_crossentropy_loss.cpp
+++ b/src/frontends/onnx/frontend/src/op/softmax_crossentropy_loss.cpp
@@ -3,75 +3,110 @@
 //
 
 #include "core/operator_set.hpp"
-#include "exceptions.hpp"
-#include "openvino/op/constant.hpp"
 #include "openvino/op/convert.hpp"
+#include "openvino/op/divide.hpp"
 #include "openvino/op/gather.hpp"
 #include "openvino/op/log.hpp"
 #include "openvino/op/multiply.hpp"
 #include "openvino/op/negative.hpp"
+#include "openvino/op/not_equal.hpp"
 #include "openvino/op/reduce_mean.hpp"
 #include "openvino/op/reduce_sum.hpp"
-#include "openvino/op/select.hpp"
 #include "openvino/op/softmax.hpp"
-#include "utils/common.hpp"
 #include "softmax_cross_entropy_loss.hpp"
 
 namespace ov {
 namespace frontend {
 namespace onnx {
 namespace {
-    // softmax cross entropy implementation (Shared helper fn)
-    OutputVector impl_softmax_cross_entropy(const Node& node, int64_t axis_default) {
-        const auto inputs = node.get_ov_inputs();
+OutputVector impl_softmax_cross_entropy(const Node& node, int64_t axis_default) {
+    const auto inputs = node.get_ov_inputs();
 
-        const auto scores = inputs[0];
-        const auto labels = inputs[1];
+    const auto scores = inputs[0];
+    const auto labels = inputs[1];
 
-        const auto axis = node.get_attribute_value<int64_t>("axis", axis_default);
-        const auto reduction = node.get_attribute_value<std::string>("reduction", "mean");
+    bool has_weights = inputs.size() > 2;
+    std::shared_ptr<ov::Node> weights_gather = nullptr;
 
-        // Computing softmax
-        const auto softmax = std::make_shared<ov::op::v8::Softmax>(scores, axis);
-        const auto log_softmax = std::make_shared<ov::op::v0::Log>(softmax);
+    bool has_ignore_index = node.has_attribute("ignore_index");
+    int64_t ignore_index_val = 0;
+    std::shared_ptr<ov::Node> mask = nullptr;
 
-        const auto axis_const = ov::op::v0::Constant::create(element::i64, {}, {axis});
-        const auto gathered = std::make_shared<ov::op::v8::Gather>(log_softmax, labels, axis_const);
+    if (has_ignore_index) {
+        ignore_index_val = node.get_attribute_value<int64_t>("ignore_index");
+        auto ignore_index_node = ov::op::v0::Constant::create(labels.get_element_type(), {}, {ignore_index_val});
+        auto neq = std::make_shared<ov::op::v1::NotEqual>(labels, ignore_index_node);
+        mask = std::make_shared<ov::op::v0::Convert>(neq, scores.get_element_type());
+    }
 
+    if (has_weights) {
+        const auto weights = inputs[2];
+        const auto axis_for_weights = ov::op::v0::Constant::create(element::i64, {}, {0});
+        weights_gather = std::make_shared<ov::op::v8::Gather>(weights, labels, axis_for_weights);
 
-        // Computing loss
-        std::shared_ptr<ov::Node> loss = std::make_shared<ov::op::v0::Negative>(gathered);
+        if (has_ignore_index) {
+            weights_gather = std::make_shared<ov::op::v1::Multiply>(weights_gather, mask);
+        }
+    } else if (has_ignore_index) {
+        weights_gather = mask;
+    }
 
-        // applying reduction as mentioned in https://github.com/onnx/onnx/blob/main/docs/Changelog.md#softmaxcrossentropyloss-12
+    const auto axis = node.get_attribute_value<int64_t>("axis", axis_default);
+    const auto reduction = node.get_attribute_value<std::string>("reduction", "mean");
 
-        if (reduction != "none") {
-            const auto reduce_axis = ov::op::v0::Constant::create(ov::element::i64, {1}, {0});
-            
-            loss = (reduction == "mean")
-                       ? static_cast<std::shared_ptr<ov::Node>>(
-                             std::make_shared<ov::op::v1::ReduceMean>(loss->output(0), reduce_axis, true))
-                       : static_cast<std::shared_ptr<ov::Node>>(
-                             std::make_shared<ov::op::v1::ReduceSum>(loss->output(0), reduce_axis, true));
-        }
+    const auto softmax = std::make_shared<ov::op::v8::Softmax>(scores, axis);
+    const auto log_softmax = std::make_shared<ov::op::v0::Log>(softmax);
 
-        return {loss};
-    }
-}
-namespace ai_onnx {
-    namespace opset_12 {
-    OutputVector ov::frontend::onnx::ai_onnx::opset_12::softmax_cross_entropy_loss(const Node& node) {
-        return impl_softmax_cross_entropy(node, 1);
-    }
-    ONNX_OP("SoftmaxCrossEntropyLoss", OPSET_SINCE(12), ai_onnx::opset_12::softmax_cross_entropy_loss);
-    }
-    namespace opset_13 {
-    OutputVector ov::frontend::onnx::ai_onnx::opset_13::softmax_cross_entropy_loss(const Node& node) {
-        return impl_softmax_cross_entropy(node, 1);
+    const auto axis_const = ov::op::v0::Constant::create(element::i64, {}, {axis});
+    const auto gathered = std::make_shared<ov::op::v8::Gather>(log_softmax, labels, axis_const);
+
+    std::shared_ptr<ov::Node> loss = std::make_shared<ov::op::v0::Negative>(gathered);
+
+    if (weights_gather) {
+        loss = std::make_shared<ov::op::v1::Multiply>(loss, weights_gather);
     }
 
-    ONNX_OP("SoftmaxCrossEntropyLoss", OPSET_SINCE(13), ai_onnx::opset_13::softmax_cross_entropy_loss);
+    if (reduction != "none") {
+        auto loss_shape = loss->get_output_partial_shape(0);
+        if (loss_shape.rank().is_static()) {
+            size_t loss_rank = loss_shape.rank().get_length();
+            std::vector<int64_t> reduce_axes(loss_rank);
+            std::iota(reduce_axes.begin(), reduce_axes.end(), 0);
+            auto reduce_axis = ov::op::v0::Constant::create(ov::element::i64, {reduce_axes.size()}, reduce_axes);
+
+            if (reduction == "mean") {
+                if (weights_gather) {
+                    auto loss_sum = std::make_shared<ov::op::v1::ReduceSum>(loss, reduce_axis, false);
+                    auto weight_sum = std::make_shared<ov::op::v1::ReduceSum>(weights_gather, reduce_axis, false);
+                    loss = std::make_shared<ov::op::v1::Divide>(loss_sum, weight_sum);
+                } else {
+                    loss = std::make_shared<ov::op::v1::ReduceMean>(loss, reduce_axis, false);
+                }
+            } else if (reduction == "sum") {
+                loss = std::make_shared<ov::op::v1::ReduceSum>(loss, reduce_axis, false);
+            }
+        } else {
+            OPENVINO_THROW("Dynamic rank is not supported for SoftmaxCrossEntropyLoss reduction.");
+        }
     }
-} 
+
+    return {loss};
+}
+}  // namespace
+namespace ai_onnx {
+namespace opset_12 {
+OutputVector softmax_cross_entropy_loss(const Node& node) {
+    return impl_softmax_cross_entropy(node, 1);
 }
+ONNX_OP("SoftmaxCrossEntropyLoss", OPSET_IN(12), ai_onnx::opset_12::softmax_cross_entropy_loss);
+}  // namespace opset_12
+namespace opset_13 {
+OutputVector softmax_cross_entropy_loss(const Node& node) {
+    return impl_softmax_cross_entropy(node, 1);
 }
-}
\ No newline at end of file
+ONNX_OP("SoftmaxCrossEntropyLoss", OPSET_IN(13), ai_onnx::opset_13::softmax_cross_entropy_loss);
+}  // namespace opset_13
+}  // namespace ai_onnx
+}  // namespace onnx
+}  // namespace frontend
+}  // namespace ov
\ No newline at end of file