In today’s competitive industrial environment, there is a demand for precise material manipulation. Electric discharge machining is an effective technology for working with difficult-to-mill materials. This research aims to investigate the relationship between machining parameters and process outcomes. To analyze the material removal rate, kerf width, and surface roughness of AISI420 stainless steel, we have selected four controllable machining parameters: the gap voltage, current, pulse on time, and pulse off time. The experiment will be conducted using the Taguchi technique orthogonal array (L16), and we will use the response surface approach to identify the best process parameters for reducing surface roughness (SR) and kerf width while maximizing material removal rate (MRR). We will use analysis of variance to assess the effectiveness of the mathematical models we are using. This research involves an extensive investigation to optimize responses simultaneously using the weighted grey relational approach. The findings showed that this methodology resulted in improved output responses during the EDM process, with a grey relational grade of 0.8226. The decision-making process for multi-objective optimization heavily depends on how weights are assigned to responses and the chosen optimization method. The experimental results demonstrate that the suggested strategy effectively enhances the overall performance of the machining process.