In various areas of civil engineering, the artificial neural network (ANN) model is used to solve complex problems. In this study, ANN models were used to predict the shear bearing capacity of RC shear walls. Based on the results of 160 experiments, a database was constructed that included the performance of RC shear walls under cyclic loading. One hundred and forty samples were chosen to train the ANN models, and 20 were used for validation. There were fourteen inputs parameters: concrete compressive strength, aspect ratio, axial compression ratio, vertical bar yield strength, horizontal bar yield strength, web vertical reinforcement ratio, web horizontal reinforcement ratio, boundary region vertical reinforcement ratio, boundary region horizontal reinforcement ratio, sectional area ratio, sectional height thickness ratio, total section area, wall height, and section shape. ANN1 and ANN2 were normalized in intervals of [0, 1] and [0.1, 0.9], respectively. The shear force of the RC shear walls was the output data for both models. The predictions by the ANN models and the code methods from GB 50011 and ACI 318 were compared. The results reveal that the developed models exhibit better prediction and generalization capacity for RC shear walls than the code methods.