Heart disease forecasting in healthcare is an important area of research that aims to predict and prevent cardiovascular diseases. The use of machine learning and artificial intelligence techniques to analyze large amounts of electronic health record (EHR) data has shown promising results in identifying risk factors, early detection, and treatment planning. The goal of heart disease forecasting is to improve clinical decision-making, reduce costs, and improve patient outcomes. Medical imaging and biomarkers also play a critical role in predicting heart disease, and researchers are exploring new ways to integrate these data sources with machine learning models. The use of precision medicine in heart disease forecasting can help personalize treatment plans for patients, based on their individual risk factors and genetic profiles. Heart disease forecasting has important implications for population health and chronic disease management, providing healthcare providers with a powerful tool to prevent and manage cardiovascular diseases, which remain a leading cause of death worldwide.