Heart disease is one of the leading causes of mortality worldwide, and early detection plays a crucial role in preventing life-threatening complications. This research presents a robust heart disease prediction system that employs a support vector machine (SVM) classifier integrated with deep learning techniques. The system utilizes a hybrid approach to enhance the accuracy of predictions by combining the strengths of SVM’s decision boundary classification with deep learning's capability to automatically extract features from raw data. A large dataset containing various patient records, including clinical parameters such as blood pressure, and heart rate, is used to train the model. By applying feature extraction and dimensionality reduction techniques, the system reduces computational complexity while retaining critical information. The SVM classifier is fine-tuned using grid search and cross-validation methods to optimize performance. The model is evaluated based on its accuracy, outperforming traditional models. The integration of deep learning further enhances feature recognition and improves prediction robustness, making the system highly reliable for clinical applications. This approach is a scalable and efficient solution for heart disease prediction, assisting healthcare professionals in early diagnosis and decision-making.