This precision in detecting cardiac risk at an early phase is still one of the most important requirements in the clinical decision support. The present paper suggests the development of a comprehensive analytical structure of prediction of heart disease with the help of structured clinical features on the UCI Cleveland dataset. During data preprocessing, feature transformation, feature normalization, and feature encoding as well as dimensionality reduction of features is performed to apply machine learning and deep learning. The comparison of different machine learning classifiers is conducted, and a hybrid deep learning system of CNN and LSTM is created to represent the local dependencies and longitudinal relationships that are inherent to the features of the patient. A comparative evaluation using the traditional machine learning algorithms and deep learning models validates enhanced predictive stability with enhanced generalization results of the hybrid model. It highlights the suitability of the integrated deep learning arrangements in terms of medical risk forecasting using clinically heterogeneous indicators.