Machine Learning Techniques for Predicting Road Accidents Using Traffic and Weather Data - A Detailed Report

Road accidents continue to be recognized as a critical public‐health challenge that causes substantial human and economic losses worldwide. The recent proliferation of real-time traffic sensors and meteorological observation systems has produced extensive multimodal data streams that can be leveraged by machine-learning algorithms. This survey reviews and synthesizes contemporary research on data-driven accident-prediction models that exploit historical traffic and weather data. The review covers supervised learning approaches ranging from decision-tree ensembles to graph and transformer networks, and it emphasizes recent advances published in 2025. Key data-processing practices, feature-engineering strategies, evaluation protocols, and deployment issues are examined. Gaps in scalability, interpretability, and cross-regional generalizability are identified, and research directions are proposed. The findings are intended to guide both scholars and practitioners who aim to design proactive accident-mitigation systems in intelligent-transportation contexts