Traffic accidents pose a significant global challenge, demanding precise prediction models to improve road safety and facilitate preventive measures. This study investigates machine learning-based methods for predicting traffic accidents, focusing on five models: Support Vector Machines (SVMs), K-Nearest Neighbors (KNNs), Decision Tree (DT), XGBoost, and Naive Bayes (NB). The models are evaluated using key performance metrics to identify their strengths and limitations. An integrated approach that combines multiple algorithms demonstrates superior performance, achieving a 9% improvement in accuracy compared to individual models. This highlights its potential for real-time decision-making and proactive strategies in traffic management. Future research could explore advanced ensemble deep learning techniques, such as neural network bagging and hybrid approaches, to address high-dimensional data and real-time challenges effectively. Incorporating additional features, such as environmental conditions and temporal factors, could further enhance the model’s predictive capabilities and scalability, paving the way for more robust and practical applications in traffic safety.