The worldwide statistics of heart diseases are increasing; therefore, new strategies for prevention and effective treatment are becoming important. This study seeks to develop complex solutions involving IoT and ML to increase the efficiency of diagnosing heart diseases. The study establishes an IoT-based system for health monitoring to collect essential cardiovascular data using wearable sensors. The data is processed using sophisticated machine learning methods, and the forecasted results indicate potential heart diseases. The effectiveness of the proposed system is assessed using a large dataset and diagnostic measures such as accuracy, sensitivity, and specificity, which are enhanced compared to conventional approaches. The results stress the importance of the synergy between IoT and ML to transform heart disease diagnosis and help create future intelligent healthcare systems.