Background:　Post-traumatic　cerebral　infarction　(PTCI)　is　a　severe　complication　resulting　from　traumatic　brain　injury　(TBI),　which　can　lead　to　permanent　neurological　damage　or　death.　The　investigation　of　the　factors　associated　with　PTCI　and　the　establishment　of　predictive　models　are　crucial　for　clinical　practice.　Methods:　We　made　a　retrospective　analysis　of　clinical　data　from　1484　TBI　patients　admitted　to　the　Neurosurgery　Department　of　a　provincial　hospital　from　January　2018　to　December　2023.　Predictive　factors　were　identified　using　the　Least　Absolute　Shrinkage　and　Selection　Operator　(LASSO)　and　multivariable　logistic　regression　analysis.　Several　machine　learning　(ML)　classification　models　were　developed　and　compared.　The　interpretations　of　the　ML　models＇　predictions　were　provided　by　SHAP　values.　Results:　Key　predictors　included　age,　bilateral　brain　contusions,　platelet　count,　uric　acid,　glucose,　traumatic　subarachnoid　hemorrhage,　and　surgical　treatment.　The　logistic　regression　(LR)　model　outperformed　other　ML　algorithms,　demonstrating　superior　performance　in　the　test　set　with　an　AUC　of　0.821,　accuracy　of　0.845,　Matthews　correlation　coefficient　(MCC)　of　0.264,　area　under　the　receiver　operating　characteristic　curve　(AUROC)　of　0.711,　precision　of　0.56,　and　specificity　of　0.971.　It　had　stable　performance　in　the　ten-fold　cross-validation.　Conclusion:　ML　algorithms,　integrating　demographic　and　clinical　factors,　accurately　predicted　the　risk　of　PTCI　occurrence.　Interpretations　using　the　SHAP　method　offer　guidance　for　personalized　treatment　of　different　patients,　filling　gaps　between　complex　clinical　data　and　actionable　insights.