The growing concern over driver safety has been attributed to an increase in traffic accidents caused by fatigue or drowsiness while driving. Addressing this issue, it is crucial to identify and prevent driver fatigue. To tackle this challenge, we propose an innovative approach that utilizes real-time video analysis to detect subtle indications of fatigue in drivers, such as yawning, variations in blink rate, and the duration of eye closure. In order to further enhance safety and well-being, our methodology integrates physiological data collected through an Electrocardiogram (ECG), a temperature sensor (LM35), and a pressure sensor to evaluate the driver’s health and level of alertness in abnormal situations. Our method incorporates an advanced system for detecting facial regions, which utilizes 68 key facial landmarks and extracts data from a webcam. This system not only accurately identifies the driver’s face but also tracks and analyses eye movements, facial expressions, and head tilt positions in each video frame. By combining these various facial characteristics and sensor data, our system is able to proactively detect signs of driver fatigue and promptly alert the driver through an audible fatigue warning system. This comprehensive method aims to greatly improve road safety by providing real-time fatigue detection and alert mechanisms for drivers, potentially saving lives and preventing accidents.