ObjectivesTo　develop　a　machine　learning-based　prediction　model　using　clinical　data　from　the　first　24　h　of　ICU　admission　to　enable　rapid　screening　and　early　intervention　for　sepsis　patients.MethodsThis　multicenter　retrospective　cohort　study　analyzed　electronic　medical　records　of　sepsis　patients　using　machine　learning　methods.　We　evaluated　model　performance　in　predicting　sepsis　outcomes　within　the　first　24　h　of　ICU　admission　across　US　and　Chinese　healthcare　settings.ResultsFrom　31　clinical　features,　machine　learning　models　demonstrated　significantly　better　predictive　performance　than　traditional　approaches　for　sepsis　outcomes.　While　linear　regression　achieved　low　test　scores　(0.25),　machine　learning　methods　reached　scores　of　0.78　and　AUCs　above　0.8　in　testing.　Importantly,　these　models　maintained　robust　performance　(scores　0.63-0.77)　in　external　validation.ConclusionsThe　application　of　machine　learning-based　prediction　models　for　sepsis　could　significantly　improve　patient　outcomes　through　early　detection　and　timely　intervention　in　the　critical　first　24　h　of　ICU　admission,　supporting　clinical　decision-making.