diff --git a/src/common/transformations/include/transformations/utils/utils.hpp b/src/common/transformations/include/transformations/utils/utils.hpp
index 9a2036fff1b20d..1961c35ef16594 100644
--- a/src/common/transformations/include/transformations/utils/utils.hpp
+++ b/src/common/transformations/include/transformations/utils/utils.hpp
@@ -182,7 +182,8 @@ TRANSFORMATIONS_API bool check_for_broadcast(const PartialShape& ref_shape, cons
 
 TRANSFORMATIONS_API std::shared_ptr<Node> activation(const std::string& activation_name, const Output<Node>& apply_to);
 
-TRANSFORMATIONS_API bool is_seq_len_provided(const std::shared_ptr<Node>& seq_len_input, int64_t max_seq_len);
+TRANSFORMATIONS_API bool is_seq_len_provided(const std::shared_ptr<Node>& X,
+                                             const std::shared_ptr<Node>& seq_len_input);
 
 TRANSFORMATIONS_API std::shared_ptr<Node> try_fold_unary_output(const std::shared_ptr<Node>& node);
 
diff --git a/src/common/transformations/src/transformations/op_conversions/convert_sequences_to_tensor_iterator.cpp b/src/common/transformations/src/transformations/op_conversions/convert_sequences_to_tensor_iterator.cpp
index 7d7cc8049883d0..a7e7b3c1ae1880 100644
--- a/src/common/transformations/src/transformations/op_conversions/convert_sequences_to_tensor_iterator.cpp
+++ b/src/common/transformations/src/transformations/op_conversions/convert_sequences_to_tensor_iterator.cpp
@@ -88,12 +88,11 @@ bool convert_sequence_to_ti(const std::shared_ptr<ov::Node>& sequence,
                             const ov::Output<ov::Node>& B,
                             const ov::op::RecurrentSequenceDirection& direction) {
     auto X_pshape = X.get_partial_shape();
-    if (X_pshape.size() < 2 || X_pshape[1].is_dynamic()) {
+    if (X_pshape.size() < 2) {
         return false;
     }
 
-    auto max_seq_len = X_pshape[1].get_length();
-    bool enable_mask = ov::op::util::is_seq_len_provided(seq_lengths.get_node_shared_ptr(), max_seq_len);
+    bool enable_mask = ov::op::util::is_seq_len_provided(X.get_node_shared_ptr(), seq_lengths.get_node_shared_ptr());
 
     const bool is_reverse = direction == ov::op::RecurrentSequenceDirection::REVERSE;
     std::shared_ptr<ov::Node> reverse_seq_before;
diff --git a/src/common/transformations/src/transformations/utils/utils.cpp b/src/common/transformations/src/transformations/utils/utils.cpp
index 62b1765e7ba275..6d77ecc0dbea94 100644
--- a/src/common/transformations/src/transformations/utils/utils.cpp
+++ b/src/common/transformations/src/transformations/utils/utils.cpp
@@ -128,11 +128,56 @@ std::shared_ptr<ov::Node> activation(const std::string& activation_name, const o
     }
 }
 
-bool is_seq_len_provided(const std::shared_ptr<Node>& seq_len_input, int64_t max_seq_len) {
+bool is_seq_len_provided(const std::shared_ptr<Node>& X, const std::shared_ptr<Node>& seq_len_input) {
+    auto max_seq_dim = X->get_output_partial_shape(0)[1];
+    if (max_seq_dim.is_dynamic()) {
+        // if values in seq_len input are equal to max_seq_len dim in X input
+        // then we don't need to insert Select operations
+        // supported seq_len_input:
+        // X -> ShapeOf -> Gather (max_seq_dim)  -> Optional (Broadcast)
+        std::shared_ptr<Node> input = seq_len_input;
+        auto broadcast = ov::as_type_ptr<ov::op::v3::Broadcast>(input);
+        if (broadcast) {
+            input = seq_len_input->input_value(0).get_node_shared_ptr();
+        }
+
+        auto gather = ov::as_type_ptr<ov::op::util::GatherBase>(input);
+        bool valid_gather = false;
+        if (gather) {
+            auto indices = gather->input_value(1).get_node_shared_ptr();
+            auto axis = gather->input_value(2).get_node_shared_ptr();
+            auto indices_const = ov::as_type_ptr<ov::op::v0::Constant>(indices);
+            auto axis_const = ov::as_type_ptr<ov::op::v0::Constant>(axis);
+            if (indices_const && axis_const) {
+                auto ind_values = indices_const->cast_vector<int64_t>();
+                auto axis_values = axis_const->cast_vector<int64_t>();
+                if (ind_values.size() == 1 && ind_values[0] == 1 && axis_values.size() == 1 && axis_values[0] == 0) {
+                    valid_gather = true;
+                }
+            }
+        }
+
+        if (!valid_gather) {
+            return true;
+        }
+
+        auto shape_of = ov::as_type_ptr<ov::op::util::ShapeOfBase>(gather->input_value(0).get_node_shared_ptr());
+        if (!shape_of) {
+            return true;
+        }
+
+        if (shape_of->input_value(0).get_node_shared_ptr() != X) {
+            return true;
+        }
+
+        return false;
+    }
+
+    auto max_seq_len_val = max_seq_dim.get_length();
     if (const auto& seq_len_const = std::dynamic_pointer_cast<op::v0::Constant>(seq_len_input)) {
         const auto& seq_len_values = seq_len_const->cast_vector<int64_t>();
-        return std::any_of(seq_len_values.begin(), seq_len_values.end(), [max_seq_len](const int64_t val) {
-            return val != max_seq_len;
+        return std::any_of(seq_len_values.begin(), seq_len_values.end(), [max_seq_len_val](const int64_t val) {
+            return val != max_seq_len_val;
         });
     }
     return true;
diff --git a/src/common/transformations/tests/op_conversions/convert_sequences_to_ti_test.cpp b/src/common/transformations/tests/op_conversions/convert_sequences_to_ti_test.cpp
index e140087c2dd2e8..7220157efbf781 100644
--- a/src/common/transformations/tests/op_conversions/convert_sequences_to_ti_test.cpp
+++ b/src/common/transformations/tests/op_conversions/convert_sequences_to_ti_test.cpp
@@ -798,3 +798,115 @@ TEST(TransformationTests, ConvertQuantizedGRUSequenceToTensorIterator) {
     auto res = compare_functions(f, f_ref);
     ASSERT_TRUE(res.first) << res.second;
 }
+
+TEST(TransformationTests, ConvertLSTMSequenceWithDynSeqLenToTensorIterator) {
+    std::shared_ptr<ov::Model> f(nullptr), f_ref(nullptr);
+    {
+        auto X = std::make_shared<opset5::Parameter>(element::f32, PartialShape{1, -1, 16});
+        auto Y = std::make_shared<opset5::Parameter>(element::f32, Shape{1, 1, 128});
+        auto Z = std::make_shared<opset5::Parameter>(element::f32, Shape{1, 1, 128});
+        auto shape_of = std::make_shared<opset5::ShapeOf>(X);
+        auto indices = opset5::Constant::create(element::i32, {1}, {1});
+        auto axis = opset5::Constant::create(element::i32, {}, {0});
+        auto seq_lengths = std::make_shared<opset5::Gather>(shape_of, indices, axis);
+
+        auto w_val = std::vector<float>(512 * 16, 0);
+        auto r_val = std::vector<float>(512 * 128, 0);
+        auto b_val = std::vector<float>(512, 0);
+        auto W = opset5::Constant::create(element::f32, Shape{1, 512, 16}, w_val);
+        auto R = opset5::Constant::create(element::f32, Shape{1, 512, 128}, r_val);
+        auto B = opset5::Constant::create(element::f32, Shape{1, 512}, b_val);
+
+        auto rnn_sequence = std::make_shared<opset5::LSTMSequence>(X,
+                                                                   Y,
+                                                                   Z,
+                                                                   seq_lengths,
+                                                                   W,
+                                                                   R,
+                                                                   B,
+                                                                   128,
+                                                                   op::RecurrentSequenceDirection::FORWARD);
+        auto Y_out = std::make_shared<opset5::Result>(rnn_sequence->output(0));
+        auto Ho = std::make_shared<opset5::Result>(rnn_sequence->output(1));
+        auto Co = std::make_shared<opset5::Result>(rnn_sequence->output(2));
+        Y_out->set_friendly_name("Y_out");
+        Ho->set_friendly_name("Ho");
+        Co->set_friendly_name("Co");
+
+        f = std::make_shared<ov::Model>(NodeVector{Y_out, Ho, Co}, ParameterVector{X, Y, Z});
+
+        pass::Manager m;
+        m.register_pass<ov::pass::InitNodeInfo>();
+        m.register_pass<ov::pass::ConvertLSTMSequenceToTensorIterator>();
+        m.run_passes(f);
+        ASSERT_NO_THROW(check_rt_info(f));
+    }
+
+    {
+        auto X = std::make_shared<opset5::Parameter>(element::f32, PartialShape{1, -1, 16});
+        auto Y = std::make_shared<opset5::Parameter>(element::f32, Shape{1, 1, 128});
+        auto Z = std::make_shared<opset5::Parameter>(element::f32, Shape{1, 1, 128});
+        auto squeeze_pattern = opset5::Constant::create(element::i64, Shape{1}, {1});
+        auto squeeze_y = std::make_shared<opset5::Squeeze>(Y, squeeze_pattern);
+        auto squeeze_z = std::make_shared<opset5::Squeeze>(Z, squeeze_pattern);
+
+        auto Xi = std::make_shared<opset5::Parameter>(element::f32, Shape{1, 1, 16});
+        auto Yi = std::make_shared<opset5::Parameter>(element::f32, Shape{1, 128});
+        auto Zi = std::make_shared<opset5::Parameter>(element::f32, Shape{1, 128});
+        auto seq_body_param = std::make_shared<opset5::Parameter>(element::i32, PartialShape{1});
+
+        // Body
+        auto squeeze_x = std::make_shared<opset5::Squeeze>(Xi, squeeze_pattern);
+
+        auto w_val = std::vector<float>(512 * 16, 0);
+        auto r_val = std::vector<float>(512 * 128, 0);
+        auto b_val = std::vector<float>(512, 0);
+        auto W = opset5::Constant::create(element::f32, Shape{512, 16}, w_val);
+        auto R = opset5::Constant::create(element::f32, Shape{512, 128}, r_val);
+        auto B = opset5::Constant::create(element::f32, Shape{512}, b_val);
+
+        auto rnn_cell = std::make_shared<opset5::LSTMCell>(squeeze_x, Yi, Zi, W, R, B, 128);
+
+        auto unsqueeze_pattern = opset5::Constant::create(element::i64, Shape{1}, {1});
+        auto Ho = std::make_shared<opset5::Result>(rnn_cell->output(0));
+
+        auto Co = std::make_shared<opset5::Result>(rnn_cell->output(1));
+
+        auto unsqueeze_y = std::make_shared<opset5::Unsqueeze>(rnn_cell->output(0), unsqueeze_pattern);
+        auto Y_out = std::make_shared<opset5::Result>(unsqueeze_y);
+
+        auto body = std::make_shared<Model>(OutputVector{Y_out, Ho, Co}, ParameterVector{Xi, Yi, Zi, seq_body_param});
+
+        auto tensor_iterator = std::make_shared<opset5::TensorIterator>();
+        tensor_iterator->set_body(body);
+
+        tensor_iterator->set_sliced_input(Xi, X, 0, 1, 1, -1, 1);
+        tensor_iterator->get_concatenated_slices(Y_out, 0, 1, 1, -1, 1);
+
+        tensor_iterator->set_merged_input(Yi, squeeze_y, Ho);
+        tensor_iterator->set_merged_input(Zi, squeeze_z, Co);
+
+        auto shape_of = std::make_shared<opset5::ShapeOf>(X);
+        auto indices = opset5::Constant::create(element::i32, {1}, {1});
+        auto axis = opset5::Constant::create(element::i32, {}, {0});
+        auto seq_lengths = std::make_shared<opset5::Gather>(shape_of, indices, axis);
+        tensor_iterator->set_invariant_input(seq_body_param, seq_lengths);
+
+        tensor_iterator->get_iter_value(Ho);
+        tensor_iterator->get_iter_value(Co);
+
+        auto res_ti_Y = std::make_shared<opset5::Result>(
+            std::make_shared<opset5::Unsqueeze>(tensor_iterator->output(0), unsqueeze_pattern));
+        auto res_ti_H = std::make_shared<opset5::Result>(
+            std::make_shared<opset5::Unsqueeze>(tensor_iterator->output(1), unsqueeze_pattern));
+        auto res_ti_C = std::make_shared<opset5::Result>(
+            std::make_shared<opset5::Unsqueeze>(tensor_iterator->output(2), unsqueeze_pattern));
+        res_ti_Y->set_friendly_name("Y_out");
+        res_ti_H->set_friendly_name("Ho");
+        res_ti_C->set_friendly_name("Co");
+        f_ref = std::make_shared<ov::Model>(NodeVector{res_ti_Y, res_ti_H, res_ti_C}, ParameterVector{X, Y, Z});
+    }
+
+    auto res = compare_functions(f, f_ref);
+    ASSERT_TRUE(res.first) << res.second;
+}
\ No newline at end of file
diff --git a/src/plugins/intel_cpu/src/nodes/rnn.cpp b/src/plugins/intel_cpu/src/nodes/rnn.cpp
index f453b7a5a51e0b..158b1f65967215 100644
--- a/src/plugins/intel_cpu/src/nodes/rnn.cpp
+++ b/src/plugins/intel_cpu/src/nodes/rnn.cpp
@@ -318,8 +318,9 @@ bool RNN::isSupportedOperation(const std::shared_ptr<const ov::Node>& op, std::s
                 errorMessage = "Max sequence length dimension is dynamic";
                 return false;
             }
-            auto maxSeqLen = data_pshape[maxSeqLenDimIdx].get_length();
-            if (ov::op::util::is_seq_len_provided(op->get_input_node_shared_ptr(seqLenIdx), maxSeqLen)) {
+
+            if (ov::op::util::is_seq_len_provided(op->get_input_node_shared_ptr(0),
+                                                  op->get_input_node_shared_ptr(seqLenIdx))) {
                 errorMessage = "Unsupported sequence length.";
                 return false;
             }
diff --git a/src/plugins/intel_gpu/src/plugin/transformations_pipeline.cpp b/src/plugins/intel_gpu/src/plugin/transformations_pipeline.cpp
index 10275dae95d729..60976fecafccce 100644
--- a/src/plugins/intel_gpu/src/plugin/transformations_pipeline.cpp
+++ b/src/plugins/intel_gpu/src/plugin/transformations_pipeline.cpp
@@ -376,8 +376,8 @@ void TransformationsPipeline::apply(std::shared_ptr<ov::Model> func) {
                 return lstm_seq->get_clip() == 0.0f &&
                        lstm_seq->get_activations() == std::vector<std::string>{"sigmoid", "tanh", "tanh"} &&
                        max_seq_len < 16 &&
-                       !ov::op::util::is_seq_len_provided(lstm_seq->get_input_node_shared_ptr(3),
-                                                              max_seq_len);
+                       !ov::op::util::is_seq_len_provided(lstm_seq->get_input_node_shared_ptr(0),
+                                                          lstm_seq->get_input_node_shared_ptr(3));
             }
             return false;
         };