Due　to　experimental　constraints,　traditional　strength　prediction　models　for　manufactured　sand　concrete　(MSC)　exhibit　significant　limitations　and　have　a　narrow　range　of　applicability.　To　overcome　these　limitations　and　enhance　prediction　accuracy,　this　paper,　based　on　the　widespread　application　of　machine　learning　in　concrete　performance　research,　employed　four　algorithms:　Support　Vector　Regression　(SVR),　Random　Forest　Regression　(RFR),　Gradient　Boosting　(GB),　and　Extreme　Gradient　Boosting　(XGB)　to　develop　highly　accurate　and　strongly　generalizable　models　for　predicting　the　compressive　strength　(CS)　and　splitting　tensile　strength　(STS)　ofMSC.　The　predictive　performance　of　each　model　was　evaluated,　the　influence　laws　of　various　factors　were　analyzed,　and　finally,　macroscopic　experiments　were　conducted　to　validate　the　models＇　prediction　accuracy　and　generalization　ability.　The　development　of　these　models　and　the　analysis　of　the　influence　laws　of　various　factors　on　the　strength　of　MSC　provide　a　valuable　reference　for　the　mix　design.　The　conclusions　are　as　follows:　(1)　The　XGB　model　demonstrates　the　highest　prediction　accuracy　and　strongest　generalization　ability　for　both　the　CS　and　STS　of　MSC,　achieving　R2　values　of　0.98　and　0.94　on　the　testing　set,　respectively.　(2)　The　CS　and　STS　of　MSC　are　primarily　influenced　by　four　factors:　water-binder　ratio　(W/B),　curing　age　(AGE),　cement　(C),　and　coarse　aggregate　(RI≥0.08869,　RI:　relative　importance),　showing　a　positive　correlation　with　AGE　and　C,　and　a　negative　correlation　with　W/B.　As　the　fly　ash　content,　stone　powder　content,　maximum　particle　size　of　coarse　aggregate,　and　sand　ratio　increase,　the　CS　and　STS　initially　increase　and　then　decrease.　Appropriately　increasing　the　fineness　modulus　of　manufactured　sand　is　beneficial　to　both　CS　and　STS,　while　the　addition　of　slag　is　beneficial　to　CS　and　harmful　to　STS.　(3)　The　prediction　accuracy　and　generalization　ability　of　the　XGB　model　are　verified　through　three　sets　of　macroscopic　experiments:　C30,　C35,　and　C40.　The　relative　errors　of　each　set＇s　predictions　are　less　than　8　%.　(4)　A　graphical　user　interface　is　constructed　based　on　the　XGB　model,　enhancing　its　practicality.　©　2024　Elsevier　Ltd