Heart disease is the leading cause of mortality and highlights the need for proper and early prediction methods to enhance the outcomes of patients and reduce the overall costs of healthcare. In this context, conventional machine learning models mostly face limitations in privacy and scalability and they face complications in handling biomedical datasets. This study has developed an advanced framework that integrates Hyperdimensional Computing (HDC), Federated Learning (FL), and Quantum-Inspired Neural Networks (QINNs) with differential privacy to address all the challenges. The QINNs leverage some quantum principles including entanglement and superposition in order to improve accuracy and computational efficiency. HDC delivers a robust approach for data presentation by high dimensional vector encoding whereas the FL enables the decentralized model training and ensures better data privacy as well as compliances including GDPR. The adoption of these technologies provides efficient, scalable, and secure solutions for predictive modeling in healthcare. This framework mainly addresses the limitations of the conventional approaches by combining the latest computational methods with an effective privacy measurement. Through leveraging all the innovations, the proposed framework aims to revolutionize the prediction of heart disease and early diagnosis as well as enhance clinical decision-making.