<p>Non-traditional machining (NTM) processes are designed to meet their own area of superiority while simultaneously possessing their specific applicability in many of the modern-day manufacturing industries. Some of them have also demonstrated higher precision by hybridizing with other NTM or conventional machining processes. Among hybrid NTM processes, laser-assisted jet electrochemical machining (LA-JECM) is among the most widely adopted material-removal processes for ensuring high-quality products. However, a significantly higher degree of intricacy in the machined components can only be achieved by optimizing the input parameters of those hybrid NTM processes. In this paper, an LA-JECM process is considered for parametric optimization in a multi-response human-centric group decision-making environment, treating supply voltage, electrolyte concentration, inter-electrode gap, and duty cycle as the process parameters, aiming to achieve superior material removal rate, taper, and average surface roughness as responses. To identify the ideal experimental trial having the optimal combination of the input parameters, an almost new multi-criteria decision-making (MCDM) method, i.e., alternative ranking order method accounting for two-step normalization (AROMAN), is implemented in a bipolar neutrosophic fuzzy (BNF) environment, while considering the corresponding importance and opinions from three decision makers for having more realistic solutions. The solution derived using the integrated method is subsequently validated against that obtained by past researchers, showing highly acceptable results. A sensitivity analysis is finally conducted to demonstrate the robustness of the proposed approach to varying combinations of response weights in a BNF environment.</p>