The World Health Organization (WHO) estimates that approximately 1.35 million annual fatalities are caused by road traffic accidents, representing a significant global challenge. This study focuses on creating a Machine Learning model to forecast traffic accident severity in Jordan. The objective of this study is to help reduce fatalities and economic losses caused by accidents, which can achieved using various Machin Learning classifiers like Decision Trees, Random Forests, Light Gradient Boosting Machines, Extra Trees, Bagging Classifiers, and Gradient Boosting, these models assess before and after down-sampling to deal with imbalance in accuracy, balanced accuracy, recall, precision, and F1-score metrics with and without hyper parameter tuning. The study emphasized the importance of advanced analytics in improving road safety measures and reducing accident severity. The researchers analyzed a dataset of 115,148 accidents. Factors considered included traffic volume, environmental conditions, and road geometry features. The data were segmented into urban and rural categories for customized modeling. Down-sampling improved the models' ability to detect injuries and deaths in both urban and rural areas. Hyper parameter tuning offered additional improvement in balanced recall and F1-score, particularly after down-sampling. In urban areas, the LGBM and Gradient Boosting classifiers showed the most significant gains in recall for minority classes, while in rural areas the Random Forest, Bagging, and Extra Trees classifies maintained a better balance between precision and recall. All classifiers achieved high accuracy (above 0.97) in both urban and rural areas.