Electric power steering (EPS) pose significant control challenges in autonomous vehicles due to their inherent complexity and non-linearity. This study explores the application of artificial neural network (ANN) to address these limitations. Two approaches are proposed: 1) an ANN-based identifier utilizing the backpropagation (BP) algorithm to learn the system's non-linear dynamics, and 2) an ANN-based controller leveraging the Levenberg-Marquardt (LM) algorithm to improve control performance. Our findings demonstrate the efficacy of the proposed ANN-based BP algorithm in EPS system identification achieving over 99.6% accuracy in predicting EPS system dynamics compared to the traditional approach. Additionally, the LM-learned ANN-based controller aiming a faster response and precise reference tracking compared to the traditional controller method. These advancements underscore the potential of employing ANN methodologies to optimize EPS performance in autonomous vehicles.