In recent years, compared with the traditional portland cement, environmentally friendly geopolymers have gained more attention as construction materials. This paper considered volcanic ash (VA) and ground granulated blast furnace slag (GGBFS) in different percentages (0%, 3%, 7%, and 10%) as a replacement for the conventionally used portland cement to stabilize sandy soils. NaOH and ${\mathrm{Na}}_2{\mathrm{SiO}}_3$ in different concentrations (4, 8, and $12\mathrm{M}$ ) and alkali to binder ratios (1, 1.5, 2, and 3) were used as alkali activator solutions to build new geopolymers. Samples were cured at both ambient and oven temperatures and for 1, 7, and 28 days. Unconfined compressive strength (UCS) of samples then was evaluated. Two predictive approaches, artificial neural network (ANN) modeling and the evolutionary polynomial regression technique (EPR), were applied to model UCS of geopolymerized sand samples. Regarding the high value of the coefficient of determination of the proposed ANN, 97%, and acceptable prediction errors, RMS error of 0.0439 and MAE of 0.0336, an 8-5-10-1 ANN was introduced as a more accurate tool for the prediction of UCS. Next, three-dimensional parametrical studies investigated the effects of simultaneous changes in alkali solution, binder, and curing condition parameters on UCS values of geopolymerized samples. Sensitivity analysis based on the cosine amplitude method introduced the Si/Al ratio as the parameter most affecting and VA content as the parameter least affecting the compressive strength of samples. Results were analyzed further using pH and electrical conductivity tests and interpreted based on microstructural investigations using scanning electron microscopy (SEM) images and X-ray diffraction analysis.