Abstract: Railway accidents continue to pose serious risks to public safety, critical infrastructure, and national economies. Traditional safety systems, which are often manual and reactive, struggle to address modern challenges such as derailments, collisions, and human error. These incidents not only lead to loss of life but also cause major service disruptions and significant financial damage.

To address these critical issues, this paper proposes a comprehensive model that integrates Deep Learning (DL), Machine Learning (ML), and GPS tracking to create a proactive and predictive safety framework for railway systems.

By leveraging historical accident data alongside real-time GPS-based monitoring, the system enhances safety protocols and supports smarter, faster decision-making. The DL component enables real-time anomaly detection through video surveillance and sensor data, while ML algorithms analyze past patterns to forecast high-risk situations. Meanwhile, the GPS module ensures continuous spatial tracking to mitigate the risks of collisions and derailments.

Initial results demonstrate notable improvements in accuracy and response time compared to traditional methods, indicating strong potential for real-world deployment across railway networks.

Downloads: | DOI: 10.17148/IARJSET.2025.12345