diff --git a/doc/sources/array_api.rst b/doc/sources/array_api.rst
new file mode 100644
index 0000000000..4c71e0cb88
--- /dev/null
+++ b/doc/sources/array_api.rst
@@ -0,0 +1,123 @@
+.. ******************************************************************************
+.. * Copyright 2024 Intel Corporation
+.. *
+.. * Licensed under the Apache License, Version 2.0 (the "License");
+.. * you may not use this file except in compliance with the License.
+.. * You may obtain a copy of the License at
+.. *
+.. *     http://www.apache.org/licenses/LICENSE-2.0
+.. *
+.. * Unless required by applicable law or agreed to in writing, software
+.. * distributed under the License is distributed on an "AS IS" BASIS,
+.. * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+.. * See the License for the specific language governing permissions and
+.. * limitations under the License.
+.. *******************************************************************************/
+
+.. _array_api:
+
+=================
+Array API support
+=================
+The `Array API <https://data-apis.org/array-api/latest/>`_ specification defines
+a standard API for all array manipulation libraries with a NumPy-like API.
+Intel(R) Extension for Scikit-Learn doesn't require
+`array-api-compat <https://github.com/data-apis/array-api-compat>`__ to be installed for
+functional support of the array API standard.
+In the current implementation, the functional support of array api follows the functional
+support of different array or DataFrame inputs and does not modify the precision of the
+input and output data formats unless necessary. Any array API input will be converted to host
+numpy.ndarrays and all internal manipulations with data will be done with these representations of
+the input data. DPNP's 'ndarray' and Data Parallel Control's 'usm_ndarray' have special handling
+requirements that are described in the relevant section of this document. Output values will in
+all relevant cases match the input data format.
+
+.. note::
+    Currently, only `array-api-strict <https://github.com/data-apis/array-api-strict>`__,
+    `dpctl <https://intelpython.github.io/dpctl/latest/index.html>`__, `dpnp <https://github.com/IntelPython/dpnp>`__
+    and `numpy <https://numpy.org/>`__ are known to work with sklearnex estimators.
+.. note::
+    Stock Scikit-learn’s array API support requires `array-api-compat <https://github.com/data-apis/array-api-compat>`__ to be installed.
+
+
+Support for DPNP and DPCTL
+==========================
+The functional support of input data for sklearnex estimators also extended for SYCL USM array types.
+These include SYCL USM arrays `dpnp's <https://github.com/IntelPython/dpnp>`__ ndarray and
+`Data Parallel Control usm_ndarray <https://intelpython.github.io/dpctl/latest/index.html>`__.
+DPNP ndarray and Data Parallel Control usm_ndarray contain SYCL contexts which can be used for
+`sklearnex` device offloading.
+
+.. note::
+    Current support for DPNP and DPCTL usm_ndarray data can be copied and moved to and from device in sklearnex and have
+    impacts on memory utilization.
+
+DPCTL or DPNP inputs are not required to use `config_context(target_offload=device)`.
+`sklearnex` will use input usm_ndarray sycl context for device offloading.
+
+.. note::
+    As DPCTL or DPNP inputs contain SYCL contexts, they do not require `config_context(target_offload=device)`.
+    However, the use of `config_context`` will override the contained SYCL context and will force movement
+    of data to the targeted device.
+
+
+Support for Array API-compatible inputs
+=======================================
+All patched estimators, metrics, tools and non-scikit-learn estimators functionally support Array API standard.
+Intel(R) Extension for scikit-Learn preserves input data format for all outputs. For all array inputs except
+SYCL USM arrays `dpnp's <https://github.com/IntelPython/dpnp>`__ ndarray and
+`Data Parallel Control usm_ndarray <https://intelpython.github.io/dpctl/latest/index.html>`__ all computation
+will be only accomplished on CPU unless specified by a `config_context`` with an available GPU device.
+
+Stock scikit-learn uses `config_context(array_api_dispatch=True)` for enabling Array API
+`support <https://scikit-learn.org/1.5/modules/array_api.html>`__.
+If `array_api_dispatch` is enabled and the installed Scikit-Learn version supports array API, then the original
+inputs are used when falling back to Scikit-Learn functionality.
+
+.. note::
+    Data Parallel Control usm_ndarray or DPNP ndarray inputs will use host numpy data copies when
+    falling back to Scikit-Learn since they are not array API compliant.
+.. note::
+    Functional support doesn't guarantee that after the model is trained, fitted attributes that are arrays
+    will also be from the same namespace as the training data.
+
+
+Example usage
+=============
+
+DPNP ndarrays
+-------------
+
+Here is an example code to demonstrate how to use `dpnp <https://github.com/IntelPython/dpnp>`__ arrays to
+run `RandomForestRegressor` on a GPU without `config_context(array_api_dispatch=True)`:
+
+.. literalinclude:: ../../examples/sklearnex/random_forest_regressor_dpnp.py
+	   :language: python
+
+
+.. note::
+    Functional support doesn't guarantee that after the model is trained, fitted attributes that are arrays
+    will also be from the same namespace as the training data.
+
+For example, if `dpnp's <https://github.com/IntelPython/dpnp>`__ namespace was used for training,
+then fitted attributes will be on the CPU and `numpy.ndarray` data format.
+
+DPCTL usm_ndarrays
+------------------
+Here is an example code to demonstrate how to use `dpctl <https://intelpython.github.io/dpctl/latest/index.html>`__
+arrays to run `RandomForestClassifier` on a GPU witout `config_context(array_api_dispatch=True)`:
+
+.. literalinclude:: ../../examples/sklearnex/random_forest_classifier_dpctl.py
+	   :language: python
+
+As on previous example, if `dpctl <https://intelpython.github.io/dpctl/latest/index.html>`__ Array API namespace was
+used for training, then fitted attributes will be on the CPU and `numpy.ndarray` data format.
+
+Use of `array-api-strict`
+-------------------------
+
+Here is an example code to demonstrate how to use `array-api-strict <https://github.com/data-apis/array-api-strict>`__
+arrays to run `DBSCAN`.
+
+.. literalinclude:: ../../examples/sklearnex/dbscan_array_api.py
+	   :language: python
diff --git a/doc/sources/conf.py b/doc/sources/conf.py
index 810c6534d6..b65842bfda 100755
--- a/doc/sources/conf.py
+++ b/doc/sources/conf.py
@@ -42,9 +42,9 @@
 author = "Intel"
 
 # The short X.Y version
-version = "2024.3.0"
+version = "2025.0.0"
 # The full version, including alpha/beta/rc tags
-release = "2024.3.0"
+release = "2025.0.0"
 
 
 # -- General configuration ---------------------------------------------------
diff --git a/doc/sources/index.rst b/doc/sources/index.rst
index 62055385c8..d6b52ae806 100755
--- a/doc/sources/index.rst
+++ b/doc/sources/index.rst
@@ -105,6 +105,7 @@ Enable Intel(R) GPU optimizations
    algorithms.rst
    oneAPI and GPU support <oneapi-gpu.rst>
    distributed-mode.rst
+   array_api.rst
    verbose.rst
    deprecation.rst
    
diff --git a/examples/sklearnex/dbscan_array_api.py b/examples/sklearnex/dbscan_array_api.py
new file mode 100644
index 0000000000..90584bd152
--- /dev/null
+++ b/examples/sklearnex/dbscan_array_api.py
@@ -0,0 +1,36 @@
+# ==============================================================================
+# Copyright 2024 Intel Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+import array_api_strict
+
+from sklearnex import config_context, patch_sklearn
+
+patch_sklearn()
+
+from sklearn.cluster import DBSCAN
+
+X = array_api_strict.asarray(
+    [[1.0, 2.0], [2.0, 2.0], [2.0, 3.0], [8.0, 7.0], [8.0, 8.0], [25.0, 80.0]],
+    dtype=array_api_strict.float32,
+)
+
+# Could be launched without `config_context(array_api_dispatch=True)`. This context
+# manager for sklearnex, only guarantee that in case of the fallback to stock
+# scikit-learn, fitted attributes to be from the same Array API namespace as
+# the training data.
+clustering = DBSCAN(eps=3, min_samples=2).fit(X)
+
+print(f"Fitted labels :\n", clustering.labels_)
diff --git a/tests/run_examples.py b/tests/run_examples.py
index 8f2f10ad01..8aac356baf 100755
--- a/tests/run_examples.py
+++ b/tests/run_examples.py
@@ -165,6 +165,7 @@ def check_library(rule):
 req_library["basic_statistics_spmd.py"] = ["dpctl", "mpi4py"]
 req_library["covariance_spmd.py"] = ["dpctl", "mpi4py"]
 req_library["dbscan_spmd.py"] = ["dpctl", "mpi4py"]
+req_library["dbscan_array_api.py"] = ["array_api_strict"]
 req_library["incremental_basic_statistics_dpctl.py"] = ["dpctl"]
 req_library["incremental_covariance_spmd.py"] = ["dpctl", "mpi4py"]
 req_library["incremental_linear_regression_dpctl.py"] = ["dpctl"]