This research project introduces an innovative train tracking system aimed at revolutionizing train transportation. By seamlessly integrating real-time GPS tracking, dynamic ETA predictions, adaptive response to signal lights using image processing methods to identify signal lights and adjust ETA predictions, alert systems for authorities, predictive maintenance capabilities, and passenger behavior analysis based on mobile device data, the system enhances accuracy, reliability, and efficiency both in terms of passenger experience and the overall railway system. Employing NodeMCU and GPS modules, the system gathers real-time GPS data, transmitting it to a centralized server. The image processing model identifies signal light status and adjusts ETA predictions accordingly, while an alert system identifies speed abnormalities and sufficiency concerns, promptly notifying authorities. Moreover, predictive maintenance analyzes data to identify component issues, optimizing overall performance. The system further leverages mobile device data to gauge train crowding levels, providing valuable insights to passengers for informed decision-making. Rigorous testing ensures that this comprehensive system not only enhances travel efficiency but also yields valuable insights into train crowding patterns. This data empowers transport authorities to optimize train services, ensuring passenger satisfaction and streamlined operations.