Considering driver fatigue is a major contributing factor to traffic accidents, trustworthy real-time monitoring solutions are required. In this study, a vision-based Driver Drowsiness Detection System that combines integrated hardware and image processing is presented. The system computes the Eye Aspect Ratio (EAR) to identify indicators of exhaustion and uses Python modules such as Dlib and OpenCV to detect face landmarks. An Arduino-controlled buzzer, LED, and LCD display are used to sound an alert when drowsiness is detected. When tested in both simulated and real-world scenarios, the system showed excellent responsiveness and dependability in recognising patterns of eye closure. An effective and affordable way to improve driver safety and lower occurrences linked to fatigue is provided by this comprehensive solution.