Making the right mix for geopolymer concrete (GPC) can take a lot of effort and money. It takes at least seven days to get conclusive results because the method involves several trial mixes to get the right proportions, which wastes materials. The current research concentrates on forecasting the compressive strength of GPC through the utilization of artificial neural networks (ANN) and the adaptive neuro-fuzzy inference system (ANFIS). Making geopolymer concrete requires alkaline solutions, which are costly, and throwing them away makes the cost of mix design even higher. This shows how important it is for software-based predictive algorithms to take the role of experimental methods for mix proportioning. There are eight input variables and one output variable in the compressive strength prediction model. The input variables consist of fly ash, ground granulated blast furnace slag (GGBS), fine aggregate, coarse aggregate, alkaline solution (NaOH + Na2SiO3), Na2SiO3/NaOH ratio, molarity, and alkali-to-binder ratio. The output variable is the compressive strength attained during outdoor curing. The study’s data was collected from prior studies on GPC, including 193 mix proportions obtained through an extensive literature investigation. Using this dataset, we used MATLAB software to train, test, and forecast compressive strength. The findings indicated that the ANN model exhibited marginally superior prediction accuracy relative to the ANFIS model.