Airfoil design optimization involves iterative processes, often using less accurate low-fidelity tools due to limitations on computational costs. Surrogate modeling bridges the gap between low computational costs and high-accuracy analysis tools (such as computational fluid dynamics). This study explores the benefits of a vectorized implementation of artificial neural networks (ANNs) as a surrogate modeling tool to predict the performance polar (profiles of lift, drag, and pitching moment coeffi