A　better　understanding　of　injury　severity　risk　factors　is　fundamental　to　improving　crash　prediction　and　effective　implementation　of　appropriate　mitigation　strategies.　Traditional　statistical　models　widely　used　in　this　regard　have　predefined　correlation　and　intrinsic　assumptions,　which,　if　flouted,　may　yield　biased　predictions.　The　present　study　investigates　the　possibility　of　using　the　eXtreme　Gradient　Boosting　(XGBoost)　model　compared　with　few　traditional　machine　learning　algorithms　(logistic　regression,　random　forest,　and　decision　tree)　for　crash　injury　severity　analysis.　The　data　used　in　this　study　was　obtained　from　the　traffic　safety　department,　ministry　of　transport　(MOT)　at　Riyadh,　KSA,　and　contains　13,546　motor　vehicle　collisions　along　15　rural　highways　reported　between　January　2017　to　December　2019.　Empirical　results　obtained　using　k-fold　(k　=　10)　for　various　performance　metrics　showed　that　the　XGBoost　technique　outperformed　other　models　in　terms　of　the　collective　predictive　performance　as　well　as　injury　severity　individual　class　accuracies.　XGBoost　feature　importance　analysis　indicated　that　collision　type,　weather　status,　road　surface　conditions,　on-site　damage　type,　lighting　conditions,　and　vehicle　type　are　the　few　sensitive　variables　in　predicting　the　crash　injury　severity　outcome.　Finally,　a　comparative　analysis　of　XGBoost　based　on　different　performance　statistics　showed　that　our　model　outperformed　most　previous　studies.