The precise estimation of the shear strength of reinforced concrete walls is critical for structural engineers. This projection, nevertheless, is exceedingly complicated because of the varied structural geometries, plethora of load cases, and highly nonlinear relationships between the design requirements and the shear strength. Recent related design code regulations mostly depend on experimental formulations, which have a variety of constraints and establish low prediction accuracy. Hence, different soft computing techniques are used in this study to evaluate the shear capacity of reinforced concrete walls. In particular, developed models for estimating the shear capacity of concrete walls have been investigated, based on experimental test data accessible in the relevant literature. The adaptive neuro-fuzzy inference system, the integrated genetic algorithms, and the integrated particle swarm optimization methods were used to optimize the fuzzy model’s membership function range and the results were compared to the outcomes of the random forests (RF) model. To determine the accuracy of the models, the results were assessed using several indices. Outliers in the anticipated data were identified and replaced with appropriate values to ensure prediction accuracy. The comparison of the resulting findings with the relevant experimental data demonstrates the potential of hybrid models to determine the shear capacity of reinforced concrete walls reliably and effectively. The findings revealed that the RF model with RMSE = 151.89, MAE = 111.52, and R2 = 0.9351 has the best prediction accuracy. Integrated GAFIS and PSOFIS performed virtually identically and had fewer errors than ANFIS. The sensitivity analysis shows that the thickness of the wall (bw) and concrete compressive strength ( fc) have the most and the least effects on shear strength, respectively.