This　paper　developed　an　autogenous　shrinkage　prediction　tool　with　high　accuracy　through　machine　learning　for　alkali-activated　slag-fly　ash　geopolymer.　The　influencing　factors　of　autogenous　shrinkage　of　activated　slag-fly　ash　geopolymer　paste　and　mortar　were　analyzed.　The　results　show　that　Extreme　Gradient　Boosting　(XGB)　algorithm　achieves　the　best　prediction　performance　with　R2　of　over　0.90　and　strong　generalization　ability　for　predicting　the　autogenous　shrinkage　of　alkaliactivated　slag-fly　ash　geopolymer　paste　and　mortar.　The　decrease　in　W/B,　alkali　dosage　and　slag　content　can　reduce　the　autogenous　shrinkage　of　alkali-activated　slag-fly　ash　geopolymer　paste,　while　increasing　W/B　and　decreasing　alkali　dosage　are　beneficial　to　mitigate　the　autogenous　shrinkage　of　alkali-activated　slag-fly　ash　geopolymer　mortar.　The　Graphical　User　Interface　(GUI)　used　for　autogenous　shrinkage　prediction　of　alkali-activated　slag-fly　ash　geopolymer　paste　or　mortar　was　designed,　which　can　directly　be　used　for　predicting　autogenous　shrinkage　on　the　premise　of　knowing　the　synthesis　parameters　of　geopolymer.　This　prediction　tool　can　prejudge　the　autogenous　shrinkage　of　alkali-activated　slag-fly　ash　geopolymer　materials　instead　of　preforming　cumbersome　autogenous　shrinkage　test,　which　will　significantly　reduce　the　workload　if　we　only　need　to　know　the　autogenous　shrinkage.