Cardiovascular diseases continue to remain the leading cause of morbidity in the world and, therefore, there is a need for proactive health monitoring and individualized risk management. The currently used systems are mostly restricted to siloed health parameters that do not provide a real-time, holistic analysis of predictive cardiovascular risk. This project thus suggests a smart sensor band integrated with a mobile application continuously collecting key health metrics like age, gender, cholesterol levels, systolic blood pressure, glucose, and heart rate. Using the FRS as a foundation, we apply the LSTM deep learning model to analyze this data and accurately estimate the actual cardiovascular risk level for each individual. The results are securely stored on the cloud and allow users to see real-time risk scores, as well as personalized recommendations about exercise and diet. This method will aid in better early diagnosis and promote preventative care for CVDs. It is a convenient and accessible solution for users to be able to manage their health and lower cardiovascular risks.