With　the　increasing　demand　for　the　increasing　performance　of　ultra-high-performance　concrete　(UHPC)　in　engineering　construction,　accurately　predicting　its　compressive　and　tensile　strength　and　optimising　the　material　mix　design　has　become　a　research　focus.　This　paper　proposes　a　hybrid　model　combining　a　multilayer　perceptron　(MLP)　and　LightGBM,　which　integrates　the　deep　feature　extraction　capability　of　MLP　and　the　efficient　regression　capability　of　LightGBM　to　achieve　the　high-precision　prediction　of　UHPC　compressive　and　tensile　strength.　Experimental　data　under　different　w/c　(0.18,　0.19,　0.20,　0.22),　curing　temperatures　(40　degrees,　60　degrees,　80　degrees),　and　an　ageing　period　of　56　days　were　collected　for　the　model　training　and　validation.　The　results　show　that　the　hybrid　model　outperforms　the　individual　models,　particularly　exhibiting　a　high　generalisation　capability　at　low　w/c,　with　R2　reaching　0.98　in　the　validation　and　test　sets　and　a　mean　absolute　error　(MAE)　of　only　1.02　MPa.　Finally,　the　effects　of　different　mix　proportions　and　curing　temperatures　on　the　models＇　prediction　results　are　discussed,　providing　valuable　reference　data　for　UHPC　material　design　and　engineering　applications.