This project presents a smart helmet system designed to enhance rider safety. It uses EOG sensing and machine learning to analyze and distinguish eye-related electrical signals for user interface utilization and fatigue detection. An IMU-based crash detection module identifies real accidents, and the system is designed to automatically send emergency notifications with GPS data. Moreover, a custom PCB integrates all hardware into a compact design. The final prototype demonstrates a practical and reliable wearable safety solution.