Driver drowsiness remains a critical factor contributing to road accidents globally. In this paper, we propose a novel approach to enhance driver drowsiness detection by leveraging facial landmarks and real-time heart rate monitoring. Our system utilizes the dlib library for accurate face detection and shape prediction, enabling precise tracking of facial features. By analyzing the eye aspect ratio (EAR), we implement a threshold-based approach to detect drowsiness, triggering timely alerts when potential danger is detected. Additionally, real-time heart rate monitoring provides supplementary insights into the driver's physiological state, further enhancing the system's effectiveness. We employ advanced signal processing techniques, including Gaussian pyramids and bandpass filters, to extract meaningful features from facial landmarks and heart rate data. Through comprehensive testing and validation, we fine-tune the system parameters to achieve improved performance in detecting drowsiness while minimizing false alarms. Our proposed system represents a significant advancement in driver safety technology, offering a robust solution to mitigate the risks associated with drowsy driving. By harnessing cutting-edge technologies and adaptive algorithms, we achieve a 94% accuracy in determining the calculated heart rate, aligning closely with the original heart rate.