This　study　proposes　an　effective　machine　learning-based　prediction　method　to　satisfy　the　urgent　requirement　to　anticipate　the　mechanical　properties　of　3D-printed　concrete.　The　goal　is　to　support　the　accurate　use　of　3D　printing　technology　in　the　building　sector.　We　have　successfully　created　machine　learning　models　that　can　predict　compressive　strength　and　flexural　strength　by　combining　experimental　data　from　a　variety　of　3D　printed　concrete　samples　and　carefully　preparing　the　data.　Our　study　explores　the　fundamentals　and　practicality　of　several　models,　such　as　artificial　neural　networks,　decision　trees,　random　forests,　support　vector　regression,　and　linear　regression.　We　have　made　sure　that　our　prediction　findings　are　reliable　and　scientifically　sound　by　implementing　stringent　model　training　and　validation　procedures.　With　a　correlation　coefficient　between　0.96　and　0.98　with　real　values,　experimental　results　demonstrate　the　random　forest　model＇s　remarkable　predicted　accuracy,　greatly　beyond　that　of　conventional　prediction　techniques.　The　practical　use　of　3D　printed　concrete　in　engineering　projects　is　strengthened　by　this　work,　which　also　opens　up　new　avenues　for　investigation　and　highlights　the　enormous　potential　of　machine　learning　to　improve　the　prediction　of　mechanical　properties　of　building　materials.