The study introduces the EyeDTrack Real-Time Driver Attention Monitoring System, which is intended to increase road safety by identifying distracted driving and fatigue. Using computer vision and artificial intelligence (AI) methods including Haar Cascade, CLAHE, Eye Aspect Ratio (EAR), Mouth Aspect Ratio (MAR), MobileNetV2, and LLaVA, the system detects risky behaviors such eye closure, yawning, and distracted driving. By providing real-time notifications via a mobile application, EyeDTrack lowers the risk of accidents and allows for quick driver response. While addressing the weaknesses of current expensive monitoring technology, the system helps achieve Sustainable Development Goal 3.6, which aims to reduce traffic-related injuries and fatalities, by providing Filipino drivers with an accessible, scalable, and reasonably priced replacement.