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This research develops a road accident prediction system as an integrated solution to improve road safety through the prediction of the severity of accidents, considering environmental conditions, driver behavior, and road conditions. This work combines state-of-the-art models like Gradient Boosting Classifier and Light Gradient Boosting Machine (GBM) Classifier to create a new stacking classifier that combines the strengths of multiple models for improved prediction accuracy. These models are evaluated against a variety of metrics, such as accuracy, precision, recall, and F1-score, among others. From this, it is observable that the stacking model is highly effective in predicting accident severity, thus providing useful information to the traffic authorities and policymakers on what to target to improve safety on the roads. In this respect, research attests to the promise of machine learning in accident prediction and calls for increased use of advanced algorithms in making intelligent data-driven interventions in road traffic accidents and fatalities.