Prestressed (PS) carbon fiber reinforced polymer (CFRP)-reinforced steel columns are novel multiparameter systems exhibiting complex nonlinear buckling behavior, which was investigated herein with the finite element method (FEM) and an artificial neural network (ANN). First, FEM models of the columns under axial and eccentric compression were built. The numerical and experimental results were in good agreement. Based on the validated FEM model, key parameters (CFRP initial prestressing force and supporting length) were studied, and the influencing rules on the buckling capacity and reinforcing efficiency of the reinforced columns were obtained. Then, 312 data sets from the validated FEM model covering 8 key parameters were generated using non-linear finite element calculation software ANSYS. Finally, as ANNs are good at handling highly complex and nonlinear problems, a practical ANN tool was developed for predicting the buckling capacity of PS CFRP-reinforced steel columns, which gives results with a high accuracy compared with FEM results.