This paper aims to present a new and accurate design equation by employing the applications of deep learning and mathematical modeling techniques in calculating the shear strength and the failure mode of corrugated web girders (CWG) formed by S690 high-strength steel (HSS). HSS provides slenderer and weight-efficient structures than those would be possible if ordinary-strength steels were used, while the corrugated web (CW) increases the shear stability of steel girders and eliminates the need of transverse stiffeners. Therefore, the CWGs and HSS can be combined into one structure for gaining more structural benefits for civil engineering applications. A finite element (FE) model is firstly established and verified by the available experimental data in the literature. Thereafter, 106 FE models of CWGs are generated and analyzed, then employed in deep learning and mathematical modeling techniques such as the neural network (NN) and Kriging model, respectively. This investigation helps to present a new mathematical model able to predict the shear strength of S690-CWG with almost zero mean square error. The predicted results via the new equation agree well with those via the experimental tests, FE analysis and NN predicted data. Moreover, the comparative results show that the currently proposed equation gives an accurate or even exact shear strength of S690-CWGs than thus via the existing equations in the literature. Finally, an extensive parametric study has been established to investigate the influence of the CWGs’ geometrical perimeters on the shear strength of S690-CWGs.