When　iron　ore　tailings　(IOTs)　are　used　as　supplementary　cementitious　materials,　their　strength　activity　index　is　an　important　factor　affecting　the　mechanical　properties　of　IOTs-cement　composites.　In　this　study,　four　ensemble　machine　learning　(ML)　models,　including　Gradient　Boosting　Regression　Tree　(GBRT),　EXtreme　Gradient　Boosting　(XGBoost),　Adaptive　Boosting　(Adaboost),　and　Categorical　Boosting　(Catboost),　were　used　to　predict　the　strength　activity　index　of　IOTs.　Shapley　additive　explanations　(SHAP)　were　also　utilized　to　measure　the　impact　of　influencing　elements　in　the　strength　activity　index　of　IOTs.　Results　showed　that　the　R-2　values　of　all　four　models　were　above　0.83　on　the　testing　set.　Dosage,　water-solid　ratio,　and　D-0.5　particle　size　were　found　to　be　the　most　important　factors　affecting　the　strength　activity　index　of　IOTs.　For　high-silica　IOTs,　the　strength　activity　index　increased　with　the　increase　of　dosage　when　the　dosage　was　less　than　10%,　while　when　the　dosage　was　higher　than　10%,　the　opposite　situation　occurred.　However,　for　low-silica　IOTs,　the　strength　activity　index　decreased　continuously　with　the　increase　of　dosage.　This　research　developed　a　new　approach　for　predicting　the　strength　activity　index　of　IOTs　and　provided　some　suggestions　for　the　application　of　IOTs　in　cement-based　materials.