<p>This study investigates machining parameter optimization through an integrated approach combining Fuzzy Logic and Grey Relational Analysis (GRA). Equal Channel Angular Pressing (ECAP) was employed to enhance grain refinement and produce high-strength materials, which significantly affects Surface Roughness (SR) and Material Removal Rate (MRR) during machining processes. Commercial pure aluminum samples underwent ECAP processing via Routes A and C up to the third pass, using both conventional and new ECAP dies. To evaluate machinability qualities (MRR and SR), the ECAPed aluminum samples were subjected to turning operations on a Computerized Numerical Control (CNC) lathe with carbide tools under various combinations of Feed Rate (FR), Spindle Speed (SS), and Depth of Cut (DC). Experiments followed an L27 orthogonal array design matrix. The research integrated Fuzzy Logic and GRA to maximize responses and optimize parameter settings. Using this combined approach, response graphs and tables were generated to identify the optimal parametric setting through Grey Fuzzy Reasoning Grade (GFRG). The optimization of Spindle Speed, Feed Rate, and Depth of Cut parameters successfully enhanced MRR while simultaneously decreasing SR. Validation tests using the optimized parameters confirmed that the outcomes aligned with the improved values predicted by the model. The optimization achieved remarkable results: approximately 450% improvement in MRR (from 548.4421 to 3034.599 mm³/min) and approximately 13% reduction in SR (from 0.8173 to 0.7108&#xa0;µm).</p> Graphical Abstract <p></p>