This　study　examines　the　analysis　and　treatment　of　diverse　types　of　solid　waste　alkaline-activated　cementitious　materials　utilizing　machine　learning　techniques.　To　account　for　the　variability　of　solid　waste　alkaline-activated　cementitious　materials,　three　crucial　chemical　components　(aluminum　trioxide,　calcium　oxide,　and　silicon　di-oxide)　were　chosen　as　representative　features.　A　total　of　274　pertinent　datasets　were　collected　and　split　into　training　and　testing　sets　with　an　80%　to　20%　ratio　for　analysis.　Six　machine　learning　models,　including　multi-layer　feedforward　neural　network,　genetic　algorithm　neural　network,　support　vector　machine,　random　forest,　radial　basis　function　neural　network,　and　long　short-term　memory　network,　were　employed　to　construct　predictive　models.　The　optimal　hyperparameters　were　identified　via　grid　search.　The　performance　of　the　models　was　assessed　using　the　training　and　testing　sets,　revealing　that　all　models　exhibited　strong　predictive　and　generalization　capabilities　on　both　sets.　Among　the　models,　the　support　vector　machine　model　achieved　the　highest　performance,　yielding　an　R-2　value　of　0.9054,　MAE　of　4.1460,　and　NRMSE　of　0.0997.　Through　the　utilization　of　SHAP　(Shapley　Additive　Explanations)　analysis,　the　interplay　between　factors　and　the　significance　of　features　were　examined.　Calcium　oxide,　water-to-binder　ratio,　silicon　dioxide,　modulus　of　water　glass,　and　aluminum　trioxide　were　recognized　as　the　primary　factors　that　influence　the　compressive　strength.　The　research　findings　offer　guidance　for　optimizing　the　performance　of　alkaline-activated　cementitious　materials　made　from　solid　waste.