Background:　Sepsis　associated　encephalopathy　(SAE)　is　prevalent　among　elderly　patients　in　the　ICU　and　significantly　affects　patient　prognosis.　Due　to　the　symptom　similarity　with　other　neurological　disorders　and　the　absence　of　specific　biomarkers,　early　clinical　diagnosis　remains　challenging.　This　study　aimed　to　develop　a　predictive　model　for　SAE　in　elderly　ICU　patients.　Methods:　The　data　of　elderly　sepsis　patients　were　extracted　from　the　MIMIC　IV　database　(version　3.1)　and　divided　into　training　and　test　sets　in　a　7:3　ratio.　Feature　variables　were　selected　using　the　LASSO-Boruta　combined　algorithm,　and　five　machine　learning　(ML)　models,　including　Extreme　Gradient　Boosting　(XGBoost),　Categorical　Boosting　(CatBoost),Light　Gradient　Boosting　Machine(LGBM),　Multilayer　Perceptron　(MLP),　and　Support　Vector　Machines　(SVM),　were　subsequently　developed　using　these　variables.　A　comprehensive　set　of　performance　metrics　was　used　to　assess　the　predictive　accuracy,　calibration,　and　clinical　applicability　of　these　models.　For　the　machine　learning　model　with　the　best　performance,　we　employed　the　SHapley　Additive　Explanations(SHAP)　method　to　visualize　the　model.　Results:　Based　on　strict　inclusion　and　exclusion　criteria,　a　total　of　3,156　elderly　sepsis　patients　were　enrolled　in　the　study,　with　an　SAE　incidence　rate　of　48.7%.　The　mortality　rate　of　elderly　sepsis　patients　who　developed　SAE　was　significantly　higher　than　that　of　patients　in　the　non-SAE　group　(28.78%　vs.　12.59%,　P　＜　0.001).　A　total　of　18　feature　variables　were　selected　for　the　construction　of　the　ML　model　using　the　LASSO-Boruta　combined　algorithm.　Compared　to　the　other　four　models　and　traditional　scoring　systems,　the　XGBoost　model　demonstrated　the　best　overall　predictive　performance,　with　Area　Under　the　Curve(AUC)=0.898,　accuracy=0.830,　recall=0.819,　F1-Score=0.820,　specificity=0.840,　and　Precision=0.821.　Furthermore,　the　results　from　the　Decision　Curve　Analysis　(DCA)　and　calibration　curves　demonstrated　that　the　XGBoost　model　has　significant　clinical　value　and　stable　predictive　performance.　The　ten-fold　cross-validation　method　further　confirmed　the　robustness　and　generalizability　of　the　model.　In　addition,　we　simplified　the　model　based　on　the　SHAP　feature　importance　ranking,　and　the　results　indicated　that　the　simplified　XGBoost　model　retains　excellent　predictive　ability　(AUC=0.858).　Conclusions:　The　XGBoost　model　effectively　predicts　SAE　in　elderly　ICU　patients　and　may　serve　as　a　reliable　tool　for　clinicians　to　identify　high-risk　patients.