This paper studies the increasing burden of cardiovascular diseases (CVDs) on public health and emphasizes the need for early detection and classification using advanced computational approaches. Our research introduces a novel method of combining deep learning with classical machine learning for heart disease classification. We utilize a labeled dataset from the UCI Machine Learning Repository that is preprocessed and normalized to improve data quality. A feature matrix is then built and fed into the ResNet50 model, which has been tailored to detect high-level, non-linear features from the heart disease dataset. For the final classification task, an SVM classifier is used to classify the features extracted by the ResNet50 model, leveraging SVM’s ability to provide optimal hyperplanes for efficient class separation. We conduct an extensive evaluation of our methodology using conventional evaluation metrics like accuracy, precision, recall, F1-score, and AUC. Results show that the hybrid RESNET-SVM model outperforms other conventional models across all classification metrics, achieving nearly perfect scores, including the highest AUC, indicating superior class segregation capability.