ObjectiveTo　create　and　verify　a　machine　learning　model　that　integrates　social　determinants　of　health　(SDoH)　for　assessing　post-stroke　depression　(PSD)　and　examining　the　association　between　SDoH　and　disease　outcomes.　MethodsData　were　acquired　from　the　National　Health　and　Nutrition　Examination　Survey.　Logistic　regression　was　employed　to　analyse　the　association　between　SDoH　and　PSD,　whereas　Cox　regression　was　utilized　to　assess　the　correlation　between　SDoH　and　all-cause　mortality　in　PSD.　The　Boruta　algorithm　was　employed　for　feature　selection,　and　four　machine　learning　models　were　constructed　(CatBoost,　Logistic,　Multilayer　Perceptron,　and　Random　Forest)　to　evaluate　the　predictive　effectiveness,　calibration,　and　clinical　applicability　of　these　ML　models.　SHAP　values　were　computed　to　ascertain　the　predictive　significance　of　each　feature　in　the　model　that　exhibited　the　highest　predictive　performance.　ResultsLogistic　regression　analysis　revealed　a　significant　positive　correlation　between　SDoH　and　PSD　prevalence(p　for　trend　＜　0.0001).　Compared　to　the　other　three　models,　CatBoost　(AUC　=　0.966)　demonstrated　the　best　overall　predictive　performance.　Moreover,　the　decision　curve　analysis　(DCA)　and　calibration　curve　findings　demonstrated　that　the　CatBoost　model　possessed　considerable　clinical　utility　and　consistent　predictive　efficacy.　The　ten-fold　cross-validation　method　further　confirmed　the　model＇s　robustness　and　generalization　ability.　ConclusionsA　linear　relationship　exists　between　SDoH　and　PSD,　with　CatBoost　demonstrating　the　best　performance　in　predicting　PSD.　SHAP　values　emphasize　the　importance　of　SDoH.