Traditional　mix　proportion　design　methods　are　difficult　to　consider　the　multiple　objectives,　while　the　design　of　economical　and　environmentally　friendly　alkali-activated　slag-fly　ash　geopolymer　concrete　on　the　premise　of　guaranteeing　compressive　strength　is　an　essential　and　meaningful　task.　The　novelty　of　this　paper　is　that　the　optimization　design　model　for　alkali-activated　slag-fly　ash　geopolymer　concrete　considering　28　days　compressive　strength,　cost,　and　carbon　emission　was　developed　based　on　machine　learning　and　Particle　Swarm　Optimization　(PSO)　algorithm.　The　results　show　that　Random　Forest　(RF),　GB,　and　Back　Propagation　Neural　Network　(BPNN)　can　achieve　a　good　prediction　effect　for　compressive　strength　with　R2　over　0.85　and　0.70　for　training　and　testing　set.　Slag　and　sodium　hydroxide　(NaOH)　contents　have　remarkable　effects　on　the　compressive　strength　of　alkali-activated　slag-fly　ash　geopolymer　concrete.　The　addition　of　slag　is　beneficial　to　enhancement　of　compressive　strength,　and　the　relatively　optimal　coarse　and　fine　aggregate　contents　are　1200　kg/m3　and　750　kg/m3.　It　is　noted　that　alkali-activated　geopolymer　is　suitable　for　preparation　of　high-strength　concrete.　Production　cost　is　need　to　be　paid　more　attention　in　the　optimization　process.　The　alkali-activated　slag-fly　ash　geopolymer　concretes　with　production　cost　reduction　of　7.6%-10.6%　and　carbon　emission　reduction　of　77.3%-80.7%　at　strength　grade　of　C30,　and　with　production　cost　reduction　of　22.5%-27.0%　and　carbon　emission　reduction　of　76.9%-81.3%　at　strength　grade　of　C50　are　achieved.