<p>Wire electric discharge machining (Wire EDM) technology serves in a diverse range of industrial applications. This study describes the implementation of grey relational analysis (GRA) with the goal of improving multi-response characteristics of AISI 304 stainless steel, making use of Wire EDM. The process parameters (pulse duration, pulse interval, and applied voltage) were optimized in order to get superior response attributes, namely surface roughness and material removal rate. The analysis of variance (ANOVA) approach has been employed to determine which machining factors have the most significant influence on response quality. The grey relational grade (GRG) was used to determine the ideal process parameters, followed by the execution of a confirmation test. The data indicate that increased material removal and improved surface quality in AISI 304 SS were achieved by increasing the pulse duration, widening the pulse interval, and reducing the applied voltage. Additionally, it has been shown that the use of predicted optimal parameter combinations enhances the performance of AISI 304 stainless steel by a significant 54.36%. The genetic algorithm optimization process yielded a higher predicted GRG of 0.7989 compared to the GRA-predicted GRG of 0.7130. In addition, the machined surfaces have been analyzed using scanning electron microscopy images. When using the optimal Wire EDM settings, the presence of craters, debris, and recast layer development on the machined surfaces was significantly minimized. Future study might focus on improving the Wire EDM method for manufacturing other materials or exploring new combinations of parameters and performance indicators.</p>