This study focuses on the prediction of the springback behavior and cross-sectional profile of the high-strength tube bar utilized in automobile stabilizers. To evaluate the mechanical response of the tube, tensile specimens are cut from the tube bar with an outer diameter of 11.1 mm and a thickness of 3.6 mm, in accordance with the ASTM E8/E8M standard. The strain distribution in the gauge area is then analyzed using a non-contact measurement technique with the aid of the ARAMIS system. Finite element simulations, including both bending and springback analyses, are subsequently performed with input bending angles ranging from 10 to 110 degrees at 10-degree intervals. Based on the simulation database, an artificial neural network (ANN) is further developed to predict various quality indicators, such as cross-sectional deformed profile, thickness reduction, and springback angle. The accuracy and feasibility of the ANN are finally validated by comparing the results of FE analysis and experiments, confirming its potential as a useful tool for designing and optimizing the bending of stabilizer bars.