Background　Cervical　lymph　node　metastasis　(LNM)　is　a　significant　factor　that　leads　to　a　poor　prognosis　in　laryngeal　cancer.　Early-stage　supraglottic　laryngeal　cancer　(SGLC)　is　prone　to　LNM.　However,　research　on　risk　factors　for　predicting　cervical　LNM　in　early-stage　SGLC　is　limited.　This　study　seeks　to　create　and　validate　a　predictive　model　through　the　application　of　machine　learning　(ML)　algorithms.Methods　The　training　set　and　internal　validation　set　data　were　extracted　from　the　Surveillance,　Epidemiology,　and　End　Results　(SEER)　database.　Data　from　78　early-stage　SGLC　patients　were　collected　from　Fujian　Provincial　Hospital　for　independent　external　validation.　We　identified　four　variables　associated　with　cervical　LNM　and　developed　six　ML　models　based　on　these　variables　to　predict　LNM　in　early-stage　SGLC　patients.Results　In　the　two　cohorts,　167　(47.44%)　and　26　(33.33%)　patients　experienced　LNM,　respectively.　Age,　T　stage,　grade,　and　tumor　size　were　identified　as　independent　predictors　of　LNM.　All　six　ML　models　performed　well,　and　in　both　internal　and　independent　external　validations,　the　eXtreme　Gradient　Boosting　(XGB)　model　outperformed　the　other　models,　with　AUC　values　of　0.87　and　0.80,　respectively.　The　decision　curve　analysis　demonstrated　that　the　ML　models　have　excellent　clinical　applicability.Conclusions　Our　study　indicates　that　combining　ML　algorithms　with　clinical　data　can　effectively　predict　LNM　in　patients　diagnosed　with　early-stage　SGLC.　This　is　the　first　study　to　apply　ML　models　in　predicting　LNM　in　early-stage　SGLC　patients.