Integration of quantum computing with machine learning offers new capabilities for solving complex optimization and classification tasks. This paper presents a Python framework that facilitates the development, training, and deployment of hybrid quantum-classical models. Utilizing variational quantum circuits and parameterized quantum algorithms, the toolkit supports quantum neural networks and hybrid classifiers while remaining compatible with classical deep learning libraries. Experimental results confirm the framework’s efficiency in processing quantum data and optimizing model performance, providing a modular and extensible platform for advancing quantum machine learning.