Cardiovascular diseases (CVDs) remain the leading cause of death globally, responsible for millions of fatalities each year. A key factor contributing to this high mortality rate is the delayed diagnosis of heart conditions, which often occurs only after symptoms have developed. Although existing diagnostic methods are effective, they are typically employed at later stages, limiting opportunities for preventive action. Early detection and prevention are therefore crucial for reducing heart disease risk and improving patient outcomes. This research introduces a novel approach for early heart disease detection and personalized prevention. It utilizes data from diverse sources, including wearable devices, medical records, and patient self-reports, to predict the likelihood of cardiovascular events. By applying machine learning algorithms, the system accurately assesses each individual's risk level and suggests tailored preventive strategies, such as dietary adjustments, increased physical activity, and stress management. It also recommends medical interventions when necessary, like prescriptions or further diagnostic testing. Through continuous monitoring and regularly updated recommendations, this approach aims to reduce the incidence of severe cardiovascular events, enhance patient quality of life, and lower overall healthcare costs.