<p>Wire Electrical Discharge Machining (Wire-EDM) is widely recognized for its capability to achieve precise material removal and superior surface quality However, process variations can introduce defects in mass production, necessitating extensive post-production quality inspections. This study develops an online surface roughness prediction model utilizing both multi-linear regression and Fuzzy Logic to enhance process monitoring and quality control. Duplex stainless steel 2507 was selected as the test material, and a design of experiments was conducted with three controllable parameters—wire tension, supply voltage, and table feed rate—to simulate variances and uncertainties in Wire-EDM cutting conditions. Regression analysis identified maximum voltage, current difference, and maximum feed rate as the most significant real-time monitoring inputs. Predictive models using both Multi-linear regression and Fuzzy Logic were developed, achieving prediction accuracies of 94.47% and 95.75%, respectively, with Fuzzy Logic exhibiting a stronger correlation between predicted and actual surface roughness. The proposed system provides a reliable and efficient approach for real-time surface roughness prediction in Wire-EDM quality control. Future research may explore the integration of additional sensor data or enhancement of the prediction model using advanced computational intelligence methodologies.</p>