The　complex　design　parameters　and　lack　of　standard　mix　design　methods　generate　a　difficulty　in　the　high　efficiently　preparation　of　alkali-activated　foamed　geopolymer　(AAFG).　Therefore,　this　paper　proposes　an　innovative　intelligent　design　method　of　AAFG　based　on　machine　learning　(ML)　and　Metaheuristic　Optimization　Algorithms　(MOA).　The　effects　of　twelve　factors　on　drying　density　(DD)　and　compressive　strength　(CS)　of　AAFG　are　revealed.　The　DD　and　CS　predictive　models　of　AAFG　with　high　accuracy　(R2　=　0.96　and　0.84)　and　strong　generalization　are　trained　through　Random　Forest　(RF).　The　enlargements　of　proportions　of　granulated　blast　furnace　slag　(GBFS)　and　metakaolin　(MK)　in　precursors　can　improve　the　CS　but　are　not　conducive　to　reduction　of　DD.　Higher　proportion　of　fly　ash　(FA)　in　precursors　can　efficiently　reduce　DD　but　can　weaken　the　CS.　Higher　or　lower　Na2O　content,　silicate　modulus　(Ms)　and　water　to　binder　(W/B)　can　reduce　DD　and　CS　with　the　optimal　foaming　conditions　of　20　%,　1.0　and　0.75.　More　additions　of　foaming　agents　and　foam　stabilizers　can　reduce　DD,　while　the　CS　has　a　remarkable　degradation.　Higher　or　lower　foaming　temperature　(FT)　is　both　not　beneficial　for　the　reduction　of　DD　and　enhancement　of　CS　with　the　optimal　FT　of　60　°C.　The　intelligent　design　system　of　AAFG　developed　by　RF　and　Slime　Mould　Algorithm　(SMA)　has　easy　operability,　high　efficiency,　and　good　stability.　The　optimal　preparation　parameters　can　be　efficiently　acquired　and　the　corresponding　performances　can　be　quickly　and　accurately　predicted.　©　2024　Elsevier　Ltd