Without　rigorous　testing,　it　is　challenging　to　predict　the　compressive　strength　of　concrete　directly　from　the　mix　ratio.　This　study　explored　an　optimal　method　for　predicting　the　compressive　strength　of　concrete　based　on　various　ensemble　machine　learning　methods.　The　database　was　collected　from　real　projects,　using　the　Maha-lanobis　distance　to　verify　database　quality.　Firstly,　the　background　of　ensemble　machine　learning　techniques　was　presented　typically　represented　by　random　forest　(RF),　the　adaptive　boosting　algorithm　(AdaBoost),　the　gradient　boosting　regression　tree　(GBRT),　extreme　gradient　boosting　(XGBoost),　the　light　gradient　boosting　machine　(LightGBM),　the　categorical　boosting　algorithm　(CatBoost).　Secondly,　the　K-fold　cross-validation　(K-fold　CV)　and　Tree-structured　Parzen　Estimator　(TPE)　algorithm　were　applied　to　find　the　optimal　hyperparameter　combina-tions　for　the　models.　Then,　the　models　were　compared　with　the　individual　machine　learning　models　by　using　four　evaluation　indexes,　including　the　root　mean　squared　error　(RMSE),　mean　absolute　error　(MAE),　mean　absolute　percentage　error　(MAPE),　and　the　coefficient　of　determination　(R2),　results　showed　that　the　LightGBM　model　achieved　the　best　comprehensive　performance.　The　SHapley　Additive　exPlanations　(SHAP)　approach　was　used　to　produce　both　global　and　local　explanations　for　the　predictions　of　the　LightGBM　model.　And　a　graphical　user　interface　(GUI)　was　developed　to　offer　users　a　useful　tool.　Finally,　a　conditional　generative　adversarial　network　(cGAN)　was　applied　to　address　the　issue　of　database　scarcity　or　the　lack　of　data　for　a　specific　concrete　strength　grade　in　the　database.