Multi-alloy design of high entropy alloys (HEAs), which possesses simple solid solution phase, thermodynamic stability, and various favorable properties. However, few engineering applications are presented till now. In this study, a hybrid artificial intelligence method (AIM) is used to optimize the resistance spot welding (RSW) parameters of dissimilar FeCoNiCrCu0.5 HEAs and AISI 304L stainless steel. In the proposed approach, Taguchi design method is used to obtain an initial solution for the optimal set of RSW parameters. True optimal values of current, time, and electrode force are then obtained using an artificial neural network (ANN) and genetic algorithm (GA). Analysis of microstructural and hardness properties of weldment revealed superior grain refinement in the fusion zone, compared to the base metal, resulting in enhanced hardness. This can be attributed to changes in the composition of metal caused by the dissimilar RSW, which led to fluctuations in thermal efficiency during the solidification process.