diff --git a/src/core/src/runtime/itensor.cpp b/src/core/src/runtime/itensor.cpp
index 59d6d283ac3526..6d966566c65610 100644
--- a/src/core/src/runtime/itensor.cpp
+++ b/src/core/src/runtime/itensor.cpp
@@ -28,7 +28,6 @@ bool ITensor::is_continuous() const {
     if (get_element_type().bitwidth() < 8)
         // OpenVINO doesn't support strides for lp types
         return true;
-
     const auto& shape = get_shape();
     const auto& type = get_element_type();
     std::vector<size_t> strides(shape.size());
diff --git a/src/plugins/intel_cpu/src/node.cpp b/src/plugins/intel_cpu/src/node.cpp
index bfbbc1cee8f2e7..1ca901f4b07117 100644
--- a/src/plugins/intel_cpu/src/node.cpp
+++ b/src/plugins/intel_cpu/src/node.cpp
@@ -1206,27 +1206,6 @@ bool Node::isFusedWith(Type fusedNodeType) const {
     return false;
 }
 
-InferenceEngine::Layout Node::getWeightsLayoutByDims(SizeVector dims, bool isGrouped) {
-    switch (dims.size()) {
-        case 0:
-            return InferenceEngine::Layout::SCALAR;
-        case 1:
-            return InferenceEngine::Layout::C;
-        case 2:
-            return InferenceEngine::Layout::NC;
-        case 3:
-            return InferenceEngine::Layout::CHW;
-        case 4:
-            return InferenceEngine::Layout::OIHW;
-        case 5:
-            return isGrouped ? InferenceEngine::Layout::GOIHW : InferenceEngine::Layout::OIDHW;
-        case 6:
-            return isGrouped ? InferenceEngine::Layout::GOIDHW : InferenceEngine::Layout::BLOCKED;
-        default:
-            return InferenceEngine::Layout::BLOCKED;
-    }
-}
-
 dnnl::memory::format_tag Node::getWeightsFormatTagByDims(const SizeVector& dims) const {
     switch (dims.size()) {
     case 1:
diff --git a/src/plugins/intel_cpu/src/node.h b/src/plugins/intel_cpu/src/node.h
index c2b575052759c3..25250284d6831a 100644
--- a/src/plugins/intel_cpu/src/node.h
+++ b/src/plugins/intel_cpu/src/node.h
@@ -630,7 +630,6 @@ class Node {
 
     virtual std::vector<dnnl::memory::format_tag> getAvailableFormatsForDims(const Shape& dims) const;
 
-    InferenceEngine::Layout getWeightsLayoutByDims(InferenceEngine::SizeVector dims, bool isGrouped);
     dnnl::memory::format_tag getWeightsFormatTagByDims(const InferenceEngine::SizeVector& dims) const;
 
     /**
diff --git a/src/plugins/intel_cpu/src/nodes/adaptive_pooling.cpp b/src/plugins/intel_cpu/src/nodes/adaptive_pooling.cpp
index 0feec97c849245..e3ac5168fef529 100644
--- a/src/plugins/intel_cpu/src/nodes/adaptive_pooling.cpp
+++ b/src/plugins/intel_cpu/src/nodes/adaptive_pooling.cpp
@@ -105,10 +105,6 @@ void AdaptivePooling::initSupportedPrimitiveDescriptors() {
     // we supports only fp32 currently
     precision = Precision::FP32;
 
-    InferenceEngine::LayerConfig config;
-    config.inConfs.resize(2);
-    config.outConfs.resize((algorithm == Algorithm::AdaptivePoolingAvg ? 1 : 2));
-
     std::vector<LayoutType> dataFormats{ LayoutType::ncsp };
     const auto &inDims = getInputShapeAtPort(0).getDims();
     if (inDims[1] != Shape::UNDEFINED_DIM && inDims[1] != 1) {
diff --git a/src/plugins/intel_cpu/src/nodes/proposal.cpp b/src/plugins/intel_cpu/src/nodes/proposal.cpp
index c0c77977461e68..92c29f6ae562c2 100644
--- a/src/plugins/intel_cpu/src/nodes/proposal.cpp
+++ b/src/plugins/intel_cpu/src/nodes/proposal.cpp
@@ -191,7 +191,7 @@ void Proposal::execute(dnnl::stream strm) {
 
         InferenceEngine::Extensions::Cpu::XARCH::proposal_exec(probabilitiesData, anchorsData, inProbDims,
                 {imgHeight, imgWidth, scaleHeight, scaleWidth}, anchors.data(), roi_indices.data(), outRoiData, outProbData, conf);
-    } catch (const InferenceEngine::Exception& e) {
+    } catch (const ov::Exception& e) {
         std::string errorMsg = e.what();
         IE_THROW() << errorMsg;
     }
diff --git a/src/plugins/intel_cpu/src/plugin.cpp b/src/plugins/intel_cpu/src/plugin.cpp
index 530c7fad0bcdb3..966411d9507a7d 100644
--- a/src/plugins/intel_cpu/src/plugin.cpp
+++ b/src/plugins/intel_cpu/src/plugin.cpp
@@ -853,7 +853,7 @@ ov::SupportedOpsMap Engine::query_model(const std::shared_ptr<const ov::Model>&
             std::unique_ptr<Node> ptr;
             try {
                 ptr.reset(Node::factory().create(op, context));
-            } catch (const InferenceEngine::Exception&) {
+            } catch (const ov::Exception&) {
                 return false;
             }
             return true;