The increasing frequency of road traffic accidents worldwide poses significant challenges in economic, societal, and public health areas, resulting in millions of injuries and deaths each year. This research utilizes machine learning methods to forecast the severity of accidents and identify primary contributing factors, using data from Ethiopia. To address the imbalance in these datasets, the Synthetic Minority Oversampling Technique (SMOTE) is applied to ensure balanced data representation. For predicting accident severity, Random Forest and XGBoost is used, with Explainable Artificial Intelligence (XAI) methods-namely Shapley Additive eXplanations (SHAP) for overarching insights and Local Interpretable Model-agnostic Explanations (LIME) for detailed, localized interpretations. This study enhances our understanding of road traffic accidents and helps mitigate their effects through sophisticated analytical approaches, providing valuable insights for policymakers, urban planners, and public health officials worldwide.