In the present study, a new model is derived to estimate the shear capacity of high-STRENGTH concrete slender beams without TRANSVERSE reinforcement using hybrid adaptive neuro-fuzzy inference system (ANFIS) and particle swarm optimization (PSO) based on the wide range of experimental results. The proposed model relates the shear capacity of beam to effective depth, compressive STRENGTH of concrete, percent of longitudinal reinforcement, ratio of shear span to effective depth, and nominal maximum size of coarse aggregate. The experimental data are randomly categorized into two subsets of the training set and test set. After establishing the proposed model, a sensitivity analysis was carried out to assess the validity of proposed ANFIS-PSO model. For this purpose, the results of the proposed model are calculated by considering the variation of the two selected input parameters, whereas the values of other parameters are fixed at the corresponding median values. To check reliability of the proposed model more accurately, the predicted values are compared with the codes and standards such as: ACI 318-14, Eurocode-2, CEB-FIP Model Code, AS 3600-2009, and JSCE Guidelines against the whole experimental specimens based on the three well-known statistical measures,correlation coefficient (R^2), root mean squared error (RMSE), and mean absolute percentage error (MAPE). It can be found that the proposed ANFIS-PSO model passed desired conditions and could estimate the shear capacity of the high-STRENGTH concrete slender beams without TRANSVERSE reinforcement with a good degree of accuracy.