<p>Crack repair methods play an essential role in maintaining structural integrity of components. This study focuses on the interference fit pin (IFP) method and its potential for effective crack repair and mitigation to extend fatigue life. Basically, this research focuses on exploring the parameters involved in the IFP technique, including crack length, hole diameters, sample thickness, and IFP distance. The goal of this investigation is to determine the optimum combination of these parameters to achieve a maximum compressive residual stress at both surface and depth. To accomplish this, a novel approach using artificial neural networks (ANNs) is proposed. This methodology enables us to analyze various parameters and identify the optimum configuration to attain the desired maximum compressive residual stress. Using a comprehensive three-dimensional simulation with Abaqus software, the performance of the IFP method and the potential for improving crack repair are evaluated. The main findings of this research indicate that by employing the proposed ANNs approach, the IFP parameters can be efficiently analyzed, and an optimal solution can be obtained. The maximum compressive residual stress obtained with this optimized configuration demonstrates the significant improvement potential for crack repair.</p>