The demand for Fiber Reinforced Polymer (FRP) bars in reinforced concrete (RC) members has substantially grown in recent decades due to FRP’s superior durability, high strength-to-weight ratio, and corrosion resistance. This paper suggests using machine learning approach to addresses the design and analysis of a simply supported deep beam with FRP bars. With a comprehensive synthetic database of 200,000 simply supported deep beam configurations that was generated based on ACI & Eurocode 2 strut-and-tie approach to explore the load path method. Several Artificial Neural Network (ANN) algorithms, including feedforward networks and ensemble methods, were trained and evaluated. The findings revealed exceptionally high predictive accuracy, achieving errors consistently below 1.7% for internal forces and below 0.1% for geometric outputs comparing to a new unseen data. From a practical standpoint, the ability of these ANN models to generate highly accurate predictions in near-real time can significantly streamline the design process, particularly when applied to FRP-reinforced deep beams, whose corrosion resistance and strength-to-weight ratio already offer notable advantages. The proposed approach demonstrates significant potential for rapid, automated structural analysis, improving both the accuracy and efficiency of FRP-reinforced deep beam design.