In　this　paper,　the　prediction　of　flexural　strength　was　investigated　using　machine　learning　methods　for　concrete　containing　supplementary　cementitious　materials　such　as　silica　fume.　First,　based　on　a　database　of　suitable　characteristic　parameters,　the　flexural　strength　prediction　was　carried　out　using　linear　(LR)　model,　random　forest　(RF)　model,　and　extreme　gradient　boosting　(XGB)　model.　Subsequently,　the　influence　of　each　input　parameter　on　the　flexural　strength　was　analyzed　using　the　SHAP　model　based　on　the　optimal　prediction　model.　The　results　showed　that　LR,　RF,　and　XGB　enhanced　the　accuracy　of　forecasting　sequentially.　Among　the　characteristic　parameters,　the　most　significant　effect　on　the　flexural　strength　of　concrete　is　the　water-binder　ratio,　and　the　water-binder　ratio　shows　a　negative　correlation　with　flexural　strength.　The　effect　of　maintenance　age　on　flexural　strength　is　second　only　to　the　water-binder　ratio,　and　it　shows　a　positive　trend.　When　the　amount　of　fly　ash　is　less　than　40%　and　the　amount　of　slag　or　silica　fume　is　less　than　30%,　the　correlation　between　the　amount　of　supplementary　cementitious　materials　and　flexural　strength　fluctuates　and　a　positive　peak　in　flexural　strength　is　observed.　However,　at　a　dosage　greater　than　the　above,　the　supplementary　cementitious　materials　all　reduce　flexural　strength.　The　interaction　interval　and　the　degree　of　interaction　between　the　supplementary　cementitious　materials　and　the　cement　content　also　differ　in　predicting　flexural　strength.