Early prediction of heart diseases is one of the most pressing challenges in healthcare, given the significant threat these diseases pose to patients' lives worldwide and the increasing incidence rates. In this context, modern medical systems are increasingly leveraging artificial intelligence (AI) and clinical data analysis to build accurate and effective predictive models. These intelligent models analyze hidden patterns in patient data such as blood pressure, heart rate, cholesterol levels, ECG readings, and medical history to predict the likelihood of chronic or acute heart disease.<br />This article presents a scientific framework for designing an intelligent model based on machine learning techniques such as deep learning networks and classification algorithms, which are trained using real clinical data from a diverse set of patients. The core of the model lies in discovering indirect relationships between clinical features and cardiovascular system behavior, providing doctors and specialists with more accurate, proactive decision-support tools.<br />Furthermore, the extracted patterns from the data are analyzed to enhance prediction accuracy and improve the ability to distinguish between normal and risky conditions, significantly contributing to reducing mortality rates and emergency interventions. It is anticipated that such models will form the foundation for building smart healthcare systems based on prediction and prevention, rather than treatment alone.<br /><br />