The integration of the Internet of Things (IoT) and Artificial Intelligence (AI) has opened new frontiers in the healthcare sector, particularly in the early detection and prevention of cardiovascular diseases (CVDs), which remain a leading cause of mortality worldwide. This paper presents the design and development of an IoT-based real-time heart disease prediction system that leverages wearable sensors and AI-driven algorithms to continuously monitor and analyze vital heart parameters. The system collects real-time heart rate data through non-invasive sensors, transmits it to a cloud platform, and processes it using machine learning models trained to detect abnormal heart conditions. Upon identifying irregularities, the system instantly alerts users or healthcare providers, enabling timely medical intervention. The integration of cloud computing allows remote access to health data, thus enhancing healthcare accessibility, reducing unnecessary hospital visits, and supporting continuous health monitoring. The proposed model demonstrates a scalable, cost-effective approach to predictive and preventive healthcare, offering a promising solution for improving patient outcomes through intelligent heart monitoring and early diagnosis.