Accurate prediction of shear strength of structural engineering components can yield a magnificent information modeling and predesign process. This paper aims to determine the shear strength of steel fiber reinforced concrete beams using the application of data-intelligence models namely hybrid artificial neural network integrated with particle swarm optimization. For the considered data-intelligence models, the input matrix attribute is one of the central element in attaining accurate predictive model. Hence, various input attributes are constructed to model the shear strength “as a targeted variable”. The modeling is initiated using historical published researches steel fiber reinforced concrete beams information. Seven variables are used as input attribute combination including reinforcement ratio (\rho \mathrm{\%}), concrete compressive strength ({{\displaystyle f}}_c^{\text{'}}), fiber factor ({{\displaystyle F}}_{\mathrm{1}}), volume percentage of fiber ({{\displaystyle V}}_f), fiber length to diameter ratio ({\displaystyle {{\displaystyle l}}_f{{\displaystyle l}}_d}) effective depth (d), and shear span-to-strength ratio ({\displaystyle ad}), while the shear strength ({{\displaystyle S}}_s) is the output of the matrix. The best network structure obtained using the network having ten nodes and one hidden layer. The final results obtained indicated that the hybrid predictive model of ANN-PSO can be used efficiently in the prediction of the shear strength of fiber reinforced concrete beams. In more representable details, the hybrid model attained the values of root mean square error and correlation coefficient 0.567 and 0.82, respectively.