Traffic accidents, a leading cause of death worldwide with nearly one million fatalities annually (WHO), are often driven by fatigue-related drowsiness. Our project introduces a real-time drowsiness detection system leveraging technologies like OpenCV, Python, and machine learning to enhance safety and accuracy. Using a camera, the system monitors facial features and eye movements, Using facial landmark detection to identify 68 key points, the system calculates the Eye Aspect Ratio (EAR). Extended periods of eye closure activate an alert, and GPS-enabled location tracking enhances response by sending automated emails with the vehicle’s real-time location to pre-registered contacts. The methodology integrates image processing, real-time facial landmark detection, and a dynamic scoring system to evaluate drowsiness. With an accuracy target of over 85%, the system addresses the limitations of existing solutions while introducing innovative location-based intervention. Results highlight its potential to reduce drowsy driving incidents, ensuring safer roads.