In　this　study,　a　machine　learning　approach　was　employed　to　establish　a　predictive　model　for　the　compressive　strength　of　magnesium　silicate　hydrate　cement　(MSHC).　Sensitivity　analysis　of　the　input　parameters　was　conducted　using　Shapley　Additive　Explanations　(SHAP).　The　results　indicated　that　the　Gradient　Boosting　Regression　(GBR)　and　Extreme　Gradient　Boosting　(XGB)　models　exhibited　enhanced　precision　in　forecasting　the　compressive　strength　of　MSHC,　with　the　XGB　model　showcasing　superior　prowess　in　generalization.　Among　the　various　feature　parameters,　the　curing　age　(Age)　was　identified　as　the　most　significant　factors　influencing　the　compressive　strength　of　MSHC.　The　compressive　strength　of　MSHC　was　positively　correlated　with　the　content　of　Age,　MgO　(C-Mg),　and　SiO2　(C-Si),　and　negatively　correlated　with　the　water-cement　ratio　(W/C).　It　also　had　a　nonlinear　relationship　with　parameters　such　as　the　specific　surface　area　of　MgO　(S-Mg)　and　the　Mg/Si　ratio.　Finally,　the　optimal　parameters　for　preparing　MSHC　were　proposed,　including　an　optimal　S-Mg　value　of　approximately　22　m2/g　and　an　optimal　Mg/Si　ratio　of　1:1.　When　potassium　dihydrogen　phosphate　(K)　was　used　as　a　dispersant,　the　optimal　dosage　was　around　4.5%.　On　the　other　hand,　when　sodium　hexametaphosphate　(S)　was　used　as　a　dispersant,　a　dosage　greater　than　2.5%　resulted　in　a　certain　improvement　in　strength.　©　2023　Elsevier　Ltd