diff --git a/src/plugins/intel_cpu/src/shape_inference/memory_accessor.hpp b/src/plugins/intel_cpu/src/shape_inference/memory_accessor.hpp
new file mode 100644
index 00000000000..f75400beb68
--- /dev/null
+++ b/src/plugins/intel_cpu/src/shape_inference/memory_accessor.hpp
@@ -0,0 +1,46 @@
+// Copyright (C) 2018-2023 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+#include <vector>
+#include "cpu_memory.h"
+#include "openvino/core/shape.hpp"
+#include "tensor_data_accessor.hpp"
+
+namespace ov {
+namespace intel_cpu {
+/**
+ * @brief cpu memory accessor implementing ov::ITensorAccessor to get data as tensor from cpu container.
+ */
+class MemoryAccessor : public ov::ITensorAccessor {
+    using container_type = std::unordered_map<size_t, MemoryPtr>;
+
+public:
+    MemoryAccessor(const container_type& ptrs, const std::vector<int64_t>& ranks)
+        : m_ptrs{ptrs}, m_ranks(ranks) {}
+
+    ~MemoryAccessor() = default;
+
+    ov::Tensor operator()(size_t port) const override {
+        const auto t_iter = m_ptrs.find(port);
+        if (t_iter != m_ptrs.cend()) {
+            auto memPtr = t_iter->second;
+            // use scalar shape {} instead of {1} if required by shapeInference
+            const auto shape = (m_ranks[port] != 0) ? ov::Shape(memPtr->getStaticDims()) : ov::Shape();
+            return {InferenceEngine::details::convertPrecision(memPtr->getDesc().getPrecision()),
+                    shape,
+                    memPtr->getData()
+                   };
+        } else {
+            return {};
+        }
+    }
+
+private:
+    const container_type& m_ptrs;              //!< Pointer to cpu memory pointers with op data.
+    const std::vector<int64_t>& m_ranks;
+};
+}  // namespace intel_cpu
+}  // namespace ov
+
diff --git a/src/plugins/intel_cpu/src/shape_inference/shape_inference_ngraph.cpp b/src/plugins/intel_cpu/src/shape_inference/shape_inference_ngraph.cpp
index dde62d222f1..029ae286454 100644
--- a/src/plugins/intel_cpu/src/shape_inference/shape_inference_ngraph.cpp
+++ b/src/plugins/intel_cpu/src/shape_inference/shape_inference_ngraph.cpp
@@ -3,6 +3,8 @@
 //
 
 #include "shape_inference_ngraph.hpp"
+#include <memory>
+#include "memory_accessor.hpp"
 
 using namespace ov::intel_cpu;
 
@@ -13,34 +15,19 @@ NgraphShapeInfer::infer(
     const auto& iranks = m_shape_infer->get_input_ranks();
     IE_ASSERT(iranks.size() <= input_shapes.size()) << "Too few input shapes passed to Shape infer.";
     std::vector<StaticShapeRef> input_static_shapes;
-    std::map<size_t, ov::HostTensorPtr> input_values;
 
     input_static_shapes.reserve(input_shapes.size());
+
     for (size_t port = 0; port < iranks.size(); port++) {
         if (iranks[port] == 0) {
             input_static_shapes.emplace_back();
         } else {
             input_static_shapes.emplace_back(input_shapes[port].get());
         }
-        auto itr = data_dependency.find(port);
-        if (itr != data_dependency.end()) {
-            const auto& memPtr = itr->second;
-
-            ov::Shape shape;
-
-            // use scalar shape {} instead of {1} if required by shapeInference
-            if (iranks[port] != 0) {
-                shape = ov::Shape(memPtr->getStaticDims());
-            }
-
-            input_values[port] = std::make_shared<ngraph::runtime::HostTensor>(
-                InferenceEngine::details::convertPrecision(memPtr->getDesc().getPrecision()),
-                shape,
-                memPtr->getData());
-        }
     }
+
     // call shape inference API
-    auto shape_infer_result = m_shape_infer->infer(input_static_shapes, ov::make_tensor_accessor(input_values));
+    auto shape_infer_result = m_shape_infer->infer(input_static_shapes, MemoryAccessor(data_dependency, iranks));
 
     Result result{{}, shape_infer_result ? ShapeInferStatus::success : ShapeInferStatus::skip};