Multi-response optimization is growing increasingly important in the selection of cutting conditions to attain optimal machineability characteristics. This study utilizes a multi-response method to model and optimize surface roughness and chip thickness ratio during the minimum quantity lubrication (MQL) machining of Nimonic 90 under various cutting parameters. To analyze and establish the correlation between input and output characteristics, a combination of factorial design, response surface methodology (RSM), and the desirability function was utilized. The optimal cutting conditions were obtained at a cutting speed of 169.047 m/min, a feed rate of 0.1 mm/rev, and a depth of cut of 0.05 mm. The estimated surface roughness and chip thickness ratio were 1.055 μm and 0.736, respectively. The experimental data and model predictions for surface roughness and chip thickness ratio demonstrated a high level of agreement, with correlation coefficients of 0.9865 and 0.9994, respectively. Therefore, the use of RSM can significantly reduce the need for extensive experimental tests in machining processes.