Abstract: Railway accidents pose risks to passenger safety, infrastructure, and economic stability. Traditional accident prevention methods rely on rule-based systems and human intervention, often failing to address real-time risks effectively. This paper integrates Deep Learning (DL), Machine Learning (ML), and Global Positioning System (GPS) tracking to enhance railway accident prevention. By leveraging historical accident data and knowledge-based analysis, we propose an intelligent system capable of real-time anomaly detection, predictive maintenance, and automated decision-making.

Keywords: Artificial Intelligence, Data Processing, Deep Learning, GPS, Machine Learning

| DOI: 10.17148/IARJSET.2025.12237