Heart disease continues to be a leading cause of mortality globally, necessitating accurate and timely detection. This research presents an enhanced AI-driven web-based e-health system designed for the robust detection of cardiac conditions. The system integrates a detailed architectural framework, a comprehensive mathematical model, and rigorous data preprocessing, including advanced noise detection and mislabeled record identification. Standard evaluations on highlights datasets, such as the UCI Heart Disease Dataset, showcase the system’s high precision and reliability. The study also includes a comparative analysis with existing methodologies, highlighting the system’s superior performance. This approach paves the way for effective telemedicine deployment, particularly benefiting rural and underserved communities by providing accessible and accurate diagnostic tools.