Cardiovascular Disease still poses a serious threat to world health, needing creative methods for early detection and prevention. The paper introduces CardioSentinel, a cutting-edge system that synergizes machine learning algorithms with wearable technology to enable early identification of heart disease. The proposed solution leverages the increasing popularity of wearable devices, which offer ongoing observation of vital signs and physiological data. CardioSentinel employs a two-fold methodology to detect early signs of heart disease. Firstly, it gathers providing a full picture of a person's cardiovascular health using real-time data from wearable devices, such as heart rate, blood pressure, and activity levels. Secondly, a sophisticated machine learning framework analyzes this data to create personalized health profiles for users. By employing both supervised and unsupervised learning techniques, CardioSentinel can identify subtle patterns and deviations that might indicate the onset of heart disease even before noticeable symptoms occur.