OBJECTIVE　Glioma　is　the　most　common　form　of　brain　tumor　and　has　high　mortality.　The　Ki-67　proliferation　index,　a　vital　marker　of　cell　proliferation,　has　been　demonstrated　to　predict　tumor　classification　and　prognosis.　The　aim　of　this　study　was　to　develop　and　validate　a　noninvasive　model　based　on　machine　learning　(ML)　and　routine　laboratory　parameters　to　preoperatively　predict　the　level　of　Ki-67　in　gliomas.　METHODS　A　total　of　506　patients　with　pathological　confirmation　of　glioma　from　2　medical　centers　(January　2020　to　December　2023)　were　retrospectively　enrolled　and　divided　into　training　(n　=　352),　internal　validation　(n　=　88),　and　external　validation　(n　=　66)　cohorts.　According　to　the　Ki-67　proliferation　index,　patients　were　classified　into　low　Ki-67　(index　＜　10%)　and　high　Ki-67　(index　＞=　10%)　groups.　Laboratory　parameters　were　obtained　within　1　week　before　surgery　from　the　Laboratory　Information　System.　The　potential　features　associated　with　Ki-67　levels　were　screened　using　extreme　gradient　boosting　(XGBoost),　support　vector　machine　(SVM),　and　least　absolute　shrinkage　and　selection　operator　(LASSO).　Then,　10　ML　classifiers,　including　SVM,　XGBoost,　logistic　regression　(LR),　random　forest,　adaptive　boosting　(AdaBoost),　gradient　boosting　machine,　partitioning　around　medoids,　naive　Bayes,　neural　network,　and　bagged　classification　and　regression　trees　(CART),　were　trained.　The　performance　of　these　models　was　evaluated　on　internal　and　external　validation　sets　using　the　area　under　the　receiver　operating　characteristic　curve　(AUC).　Calibration　curve,　decision　curve,　and　clinical　impact　curve　analyses　were　used　for　validation.　RESULTS　Fifteen　laboratory　parameters　that　met　the　requirements　of　XGBoost,　SVM,　and　LASSO　were　selected.　Among　all　tested　ML　models,　the　LR　model　had　superior　performance　with　relatively　high　AUC,　accuracy,　sensitivity,　and　specificity.　The　LR　model　achieved　AUCs　of　0.838　in　the　training　set,　0.800　(with　the　highest　accuracy　[0.782]　and　optimal　sensitivity　[0.845])　in　the　internal　validation　set,　and　0.757　in　the　external　validation　set.　Finally,　the　LR　model　was　visualized　as　a　nomogram　based　on　the　top　6　laboratory　parameters　(age,　anion　gap,　apolipoprotein　A-1,　apolipoprotein　B,　calcium,　creatinine)　to　individually　predict　the　Ki-67　proliferation　index　in　patients　with　gliomas.　CONCLUSIONS　The　authors　successfully　constructed　an　LR　model　based　on　routine　laboratory　parameters,　with　relatively　high　sensitivity　and　specificity,　to　preoperatively　predict　the　level　of　Ki-67　in　patients　with　gliomas,　which　might　be　helpful　for　prognostic　evaluation　and　clinical　decision-making.