This　paper　employs　Support　Vector　Regression　(SVR),　Random　Forest　Regression　(RF),　Gradient　Boosting　(GB),　and　Extreme　Gradient　Boosting　(XGB)　algorithms　to　establish　the　compressive　strength　prediction　models　for　Recycled　Aggregate　Concrete　(RAC)　and　analyze　the　influence　of　ten　inputs　on　RAC　compressive　strength.　Combined　with　the　best　prediction　model,　the　Non-dominated　Sorting　Genetic　Algorithm　II　(NSGA-II)　is　applied　for　multi-objective　optimization　of　mixture　proportions　in　RAC　addressing　cost,　carbon　emissions,　and　compressive　strength　as　key　objectives.　The　results　demonstrate　that　the　RAC　compressive　strength　prediction　model　using　the　GB　algorithm　exhibits　the　highest　accuracy,　with　the　R2　value　of　0.99　for　the　training　set　and　0.85　for　the　testing　set.　Feature　importance　and　SHAP　analysis　reveal　that　cement　and　water　contents　are　the　primary　factors　affecting　the　compressive　strength　of　RAC.　Among　the　three　mineral　admixtures　including　Silica　Fume　(SF),　Fly　Ash　(FA),　and　Ground　Granulated　Blast　Furnace　Slag　(GGBFS),　SF　exhibits　superior　improvement　in　the　compressive　strength　of　RAC　compared　to　FA　and　GGBFS.　Partial　dependency　plot　analysis　indicates　that　concrete　strength　remains　unaffected　by　recycled　coarse　aggregate　content　within　the　range　of　0　to　300　kg/m3.　Pareto　fronts　of　a　tri-objective　mixture　optimization　problem　for　RAC　are　successfully　obtained　through　the　GB　model　combined　with　the　NSGA-II　algorithm,　which　can　guide　the　optimization　of　RAC　preparation.