Aims:　Although　there　are　various　model-based　approaches　to　individualized　vancomycin　(VCM)　administration,　few　have　been　reported　for　adult　patients　with　periprosthetic　joint　infection　(PJI).　This　work　attempted　to　develop　a　machine　learning　(ML)-based　model　for　predicting　VCM　trough　concentration　in　adult　PJI　patients.　Methods:　The　dataset　of　287　VCM　trough　concentrations　from　130　adult　PJI　patients　was　split　into　a　training　set　(229)　and　a　testing　set　(58)　at　a　ratio　of　8:2,　and　an　independent　external　32　concentrations　were　collected　as　a　validation　set.　A　total　of　13　covariates　and　the　target　variable　(VCM　trough　concentration)　were　included　in　the　dataset.　A　covariate　model　was　respectively　constructed　by　support　vector　regression,　random　forest　regression　and　gradient　boosted　regression　trees　and　interpreted　by　SHapley　Additive　exPlanation　(SHAP).　Results:　The　SHAP　plots　visualized　the　weight　of　the　covariates　in　the　models,　with　estimated　glomerular　filtration　rate　and　VCM　daily　dose　as　the　2　most　important　factors,　which　were　adopted　for　the　model　construction.　Random　forest　regression　was　the　optimal　ML　algorithm　with　a　relative　accuracy　of　82.8%　and　absolute　accuracy　of　67.2%　(R2 =.61,　mean　absolute　error =　2.4,　mean　square　error =　10.1),　and　its　prediction　performance　was　verified　in　the　validation　set.　Conclusion:　The　proposed　ML-based　model　can　satisfactorily　predict　the　VCM　trough　concentration　in　adult　PJI　patients.　Its　construction　can　be　facilitated　with　only　2　clinical　parameters　(estimated　glomerular　filtration　rate　and　VCM　daily　dose),　and　prediction　accuracy　can　be　rationalized　by　SHAP　values,　which　highlights　a　profound　practical　value　for　clinical　dosing　guidance　and　timely　treatment.　©　2024　British　Pharmacological　Society.