The　glass　fiber-reinforced　polymer　(GFRP)　rebar　reinforced　cemented　soil　is　widely　employed　to　solve　the　weak　foundation　problem　led　by　sludge　particularly.　The　robustness　of　this　structure　is　highly　dependent　on　the　interface　bond　strength　between　the　GFRP　tendon　and　cemented　soils.　However,　its　application　is　obstructed　owing　to　the　deficient　studies　on　the　influence　factors.　Therefore,　this　study　investigates　the　effects　of　water　content　(Cw:　50%–90%),　cement　proportion　(Cc:　6%–30%),　and　curing　period　(Tc:　28–90　days)　on　peak　and　residual　interface　bond　strengths　(Tp　and　Tt),　as　well　as　the　unconfined　compression　strength　(UCS).　Results　indicated　that　mechanical　properties　were　positively　responded　to　Tc　and　Cc,　while　negatively　correlated　to　Cw.　Besides,　Random　Forest　(RF),　one　of　the　machine　learning　(ML)　models,　was　developed　with　its　hyperparameters　tuned　by　the　firefly　algorithm　(FA)　based　on　the　experimental　dataset.　The　pullout　strength　was　predicted　by　the　ML　model　for　the　first　time.　High　correlation　coefficients　and　low　root-mean-square　errors　verified　the　accuracy　of　established　RF-FA　models　in　this　study.　Subsequently,　a　coFA-based　multi-objective　optimisation　firefly　algorithm　(MOFA)　was　introduced　to　optimise　tri-objectives　between　UCS,　Tp　(or　Tt),　and　cost.　The　Pareto　fronts　were　successfully　acquired　for　optimal　mixture　designs,　which　contributes　to　the　application　of　GFRP　tendon　reinforced　cemented　soil　in　practice.　In　addition,　the　sensitivity　of　input　variables　was　evaluated　and　ranked.　©　2022　The　Authors