Objective　Early　prediction　of　long-term　outcomes　in　patients　with　systemic　lupus　erythematosus　(SLE)　remains　a　great　challenge　in　clinical　practice.　Our　study　aims　to　develop　and　validate　predictive　models　for　the　mortality　risk.Methods　This　observational　study　identified　patients　with　SLE　requiring　hospital　admission　from　the　Medical　Information　Mart　for　Intensive　Care　(MIMIC-IV)　database.　We　downloaded　data　from　Fujian　Provincial　Hospital　as　an　external　validation　set.　Variable　selection　was　performed　using　the　Least　Absolute　Shrinkage　and　Selection　Operator　(LASSO)　regression.　Then,　we　constructed　two　predictive　models:　a　traditional　nomogram　based　on　logistic　regression　and　a　machine　learning　model　employing　a　stacking　ensemble　approach.　The　predictive　ability　of　the　models　was　evaluated　by　the　areas　under　the　receiver　operating　characteristic　curve　(AUC)　and　the　calibration　curve.Results　A　total　of　395　patients　and　100　patients　were　enrolled　respectively　from　MIMIC-IV　database　and　the　validation　cohort.　The　LASSO　regression　identified　18　significant　variables.　Both　models　demonstrated　good　discrimination,　with　AUCs　above　0.8.　The　machine　learning　model　outperformed　the　nomogram　in　terms　of　precision　and　specificity,　highlighting　its　potential　superiority　in　risk　prediction.　The　SHapley　additive　explanations　analysis　further　elucidated　the　contribution　of　each　variable　to　the　model＇s　predictions,　emphasising　the　importance　of　factors　such　as　urine　output,　age,　weight　and　alanine　aminotransferase.Conclusions　The　machine　learning　model　provides　a　superior　tool　for　predicting　mortality　risk　in　patients　with　SLE,　offering　a　basis　for　clinical　decision-making　and　potential　improvements　in　patient　outcomes.