In　this　study,　a　compressive　strength　prediction　model　for　Magnesium　Phosphate　Cement　containing　solid　waste　materials　(SW-MPC)　was　developed　using　machine　learning　(ML)　techniques.　Firstly,　a　database　of　SW-MPC　compressive　strength　was　established　through　the　selection　of　solid　waste　materials　based　on　their　chemical　compositions.　Subsequently,　various　ML　algorithms　were　employed　to　construct　predictive　models　for　SW-MPC　compressive　strength,　and　the　predictive　accuracy　of　these　models　was　compared.　Furthermore,　Shapley　Additive　Explanations　(SHAP)　analysis　was　utilized　to　assess　the　impact　of　various　parameters　on　SW-MPC　compressive　strength.　The　results　revealed　that　the　Extreme　Gradient　Boosting　(XGB)　model　exhibited　the　highest　predictive　accuracy　and　generalization　ability.　Among　numerous　feature　parameters,　the　curing　age　(Age)　and　phosphate　type　(Type_PO4)　were　identified　as　the　most　critical　factors　influencing　SW-MPC　compressive　strength.　SW-MPC　compressive　strength　exhibited　a　positive　correlation　with　the　Si　content　(Con_Si)　and　Al　content　(Con_Al)　in　the　solid　waste　materials,　while　it　generally　displayed　a　negative　correlation　with　Fe　content　(Con_Fe).　The　optimal　Magnesium-to-Phosphorus　ratio　(M/P)　for　SW-MPC　preparation　ranged　from　4　to　6.　Furthermore,　the　influence　of　the　dosage　of　solid　waste　materials　(Per_sw)　on　the　compressive　strength　of　SW-MPC　was　complex,　with　the　optimal　content　being　around　10%　and　35%.　This　complexity　arose　from　the　significant　impact　of　the　interactive　effects　between　the　content　of　different　elements　in　solid　waste　materials　and　Per_sw　on　the　compressive　strength　of　SW-MPC.