This study investigates the application of Geographic Information Systems (GIS) in traffic accident analysis and prediction. By integrating GIS with deep learning techniques, the research highlights how spatial data management and analysis can enhance road safety. Key objectives include identifying accident hotspots, optimizing traffic control systems, and improving emergency response. The methodology involves a comprehensive review of existing literature, emphasizing GIS's role in data integration, spatial analysis, and predictive modeling. Findings demonstrate that GIS significantly contributes to understanding traffic patterns, predicting accidents, and formulating targeted safety interventions. Challenges such as data complexity, real-time processing, and model interpretability are addressed, offering future directions for leveraging GIS in road safety management. The study concludes that GIS, combined with advanced analytics, presents a powerful tool for reducing traffic accidents and enhancing overall traffic safety.