diff --git a/docs/sphinx_setup/api/api_reference.rst b/docs/sphinx_setup/api/api_reference.rst
index 933be0844e9c9f..867da0096b0344 100644
--- a/docs/sphinx_setup/api/api_reference.rst
+++ b/docs/sphinx_setup/api/api_reference.rst
@@ -10,13 +10,13 @@ API Reference
    :maxdepth: 2
    :hidden:
 
+   ie_python_api/api
    c_cpp_api/group__ov__cpp__api
    c_cpp_api/group__ov__c__api
-   ie_python_api/api
-
+   nodejs_api/nodejs_api.rst
 
 
-OpenVINO toolkit offers **APIs for Python, C, and C++** which share most features (C++ being the
+OpenVINO toolkit offers **APIs for Python, C++, C, and JavaScript (Node.js)** which share most features (C++ being the
 most comprehensive one), have a common structure, naming convention styles, namespaces,
 and no duplicate structures.
 
diff --git a/docs/sphinx_setup/api/nodejs_api/nodejs_api.rst b/docs/sphinx_setup/api/nodejs_api/nodejs_api.rst
new file mode 100644
index 00000000000000..adeab58d514d6f
--- /dev/null
+++ b/docs/sphinx_setup/api/nodejs_api/nodejs_api.rst
@@ -0,0 +1,223 @@
+OpenVINO Node.js API
+=====================
+
+.. meta::
+   :description: Explore Node.js API and implementation of its features in Intel®
+                 Distribution of OpenVINO™ Toolkit.
+
+
+OpenVINO Node.js API is distributed as an *openvino-node* npm package that contains JavaScript
+wrappers with TypeScript types descriptions and a script that downloads the OpenVINO Node.js
+bindings for current OS.⠀
+
+System requirements
+###################
+
+.. list-table::
+   :header-rows: 1
+
+   * - Operating System
+     - Architecture
+     - Software
+   * - Windows, Linux, macOS
+     - x86, ARM (Windows ARM not supported)
+     - `Node.js version 20.5.1 and higher <https://nodejs.org/en/download/>`__
+
+
+Install openvino-node package
+#############################
+
+To install the package, use the following command:
+
+.. code-block:: sh
+
+   npm install openvino-node
+
+
+.. note::
+
+   The *openvino-node* npm package runs in Node.js environment only and provides
+   a subset of :doc:`OpenVINO Runtime C++ API <../c_cpp_api/group__ov__cpp__api>`.
+
+
+Use openvino-node package
+#########################
+
+1. Import openvino-node package. Use the ``addon`` property to reach general exposed entities:
+
+   .. code-block:: js
+
+      const { addon: ov } = require('openvino-node');
+
+
+2. Load and compile a model, then prepare a tensor with input data. Finally, run inference
+   on the model with it to get the model output tensor:
+
+   .. code-block:: js
+
+      const { addon: ov } = require('openvino-node');
+      // Load model
+      const core = new ov.Core();
+      const model = await ov.readModel('path/to/model', 'path/to/model/weights');
+      // Compile model
+      const compiledModel = await ov.compileModel(model, 'CPU');
+      // Prepare tensor with input data
+      const tensorData = new Float32Array(image.data);
+      const shape = [1, image.rows, image.cols, 3];
+      const inputTensor = new ov.Tensor(ov.element.f32, shape, tensorData);
+      const inferRequest = compiledModel.createInferRequest();
+      const modelOutput = inferRequest.infer([inputTensor]);
+
+
+For more extensive examples of use, refer to the following scripts:
+
+- `Hello Classification Sample <https://github.com/openvinotoolkit/openvino/blob/master/samples/js/node/hello_classification/hello_classification.js>`__
+- `Hello Reshape SSD Sample <https://github.com/openvinotoolkit/openvino/blob/master/samples/js/node/hello_reshape_ssd/hello_reshape_ssd.js>`__
+- `Image Classification Async Sample <https://github.com/openvinotoolkit/openvino/blob/master/samples/js/node/classification_sample_async/classification_sample_async.js>`__
+
+OpenVINO API features
+#####################
+
+.. list-table::
+   :widths: 15 85
+   :class: nodejs-features
+
+   * - ``addon``
+     -
+       .. code-block:: ts
+
+          Core()
+          Tensor()
+          PartialShape()
+          element
+          preprocess:
+            resizeAlgorithms
+            PrePostProcessor()
+
+   * - ``CompiledModel``
+     -
+       .. code-block:: ts
+
+          outputs: Output[]
+          inputs: Output[]
+          constructor()
+          output(nameOrId?: string | number): Output
+          input(nameOrId?: string | number): Output
+          createInferRequest(): InferRequest
+
+   * - ``Core``
+     -
+       .. code-block:: ts
+
+          constructor()
+          compileModel(model: Model, device: string, config?: { [option: string]: string }): Promise<CompiledModel>
+          compileModelSync(model: Model, device: string, config?: { [option: string]: string }): CompiledModel
+          readModel(modelPath: string, binPath?: string): Promise<Model>
+          readModel(modelBuffer: Uint8Array, weightsBuffer?: Uint8Array): Promise<Model>;
+          readModelSync(modelPath: string, binPath?: string): Model
+          readModelSync(modelBuffer: Uint8Array, weightsBuffer?: Uint8Array): Model;
+
+   * - ``InferRequest``
+     -
+       .. code-block:: ts
+
+          constructor()
+          setTensor(name: string, tensor: Tensor): void
+          setInputTensor(idxOrTensor: number | Tensor, tensor?: Tensor): void
+          setOutputTensor(idxOrTensor: number | Tensor, tensor?: Tensor): void
+          getTensor(nameOrOutput: string | Output): Tensor
+          getInputTensor(idx?: number): Tensor
+          getOutputTensor(idx?: number): Tensor
+          getCompiledModel(): CompiledModel
+          inferAsync(inputData?: { [inputName: string]: Tensor |SupportedTypedArray} | Tensor[] | SupportedTypedArray[]): Promise<{ [outputName: string] : Tensor}>;
+          infer(inputData?: { [inputName: string]: Tensor |SupportedTypedArray} | Tensor[] | SupportedTypedArray[]): { [outputName: string] : Tensor};
+
+   * - ``InputInfo``
+     -
+       .. code-block:: ts
+
+          tensor(): InputTensorInfo;
+          preprocess(): PreProcessSteps;
+          model(): InputModelInfo;
+
+   * - ``InputModelInfo``
+     -
+       .. code-block:: ts
+
+          setLayout(layout: string): InputModelInfo;
+
+   * - ``InputTensorInfo``
+     -
+       .. code-block:: ts
+
+          setElementType(elementType: element | elementTypeString ): InputTensorInfo;
+          setLayout(layout: string): InputTensorInfo;
+          setShape(shape: number[]): InputTensorInfo;
+
+   * - ``Model``
+     -
+       .. code-block:: ts
+
+          outputs: Output[]
+          inputs: Output[]
+          output(nameOrId?: string | number): Output
+          input(nameOrId?: string | number): Output
+          getName(): string
+
+   * - ``Output``
+     -
+       .. code-block:: ts
+
+          anyName: string;
+          shape: number[];
+
+          constructor()
+          toString(): string
+          getAnyName(): string
+          getShape(): number[]
+          getPartialShape(): number[]
+
+   * - ``OutputInfo``
+     -
+       .. code-block:: ts
+
+          tensor(): OutputTensorInfo;
+
+   * - ``OutputTensorInfo``
+     -
+       .. code-block:: ts
+
+          setElementType(elementType: element | elementTypeString ): InputTensorInfo;
+          setLayout(layout: string): InputTensorInfo;
+
+   * - ``PrePostProcessor``
+     -
+       .. code-block:: ts
+
+          constructor(model: Model)
+          build(): PrePostProcessor
+          input(): InputInfo
+          output(): OutputInfo
+
+   * - ``preprocess.element``
+     - u8, u16, u32, i8, i16, i32, i64, f32, f64
+
+   * - ``preprocess.resizeAlgorithm``
+     - RESIZE_CUBIC, RESIZE_LINEAR
+
+   * - ``PreProcessSteps``
+     -
+       .. code-block:: ts
+
+          resize(algorithm: resizeAlgorithm | string): PreProcessSteps;
+
+   * - ``Tensor``
+     -
+       .. code-block:: ts
+
+          data: number[]
+          constructor(type: element, shape: number[], tensorData?: number[] | SupportedTypedArray): Tensor
+          getElementType(): element
+          getShape(): number[]
+          getData(): number[]
+