<p>This research investigated the machinability of AISI 02 tool steel under various lubrication conditions, focusing on the application of minimum quantity lubrication (MQL) and an innovative cupric oxide (CuO)–based nanofluid. A temperature and tool wear investigation was performed for the machining environment. A comprehensive experimental setup utilizing L36 Taguchi-based orthogonal arrays to conduct trials under dry, MQL, and NMQL (nanofluid MQL) conditions. This study meticulously examines the impact of four principal machining parameters, cutting speed, feed, environment, and cutting depth, on critical outcomes, such as surface roughness, cutting force, and power consumption. Employing response surface methodology (RSM), this research delineates the optimal machining conditions for enhancing these parameters. Notably, the feed was found to significantly affect the surface roughness, while both the cutting depth and feed were instrumental in determining the cutting force and power consumption. The use of a Cu nanofluid with MQL substantially enhanced the machining performance. This paper culminates with an exploration of cutting condition optimization through the desirability function (<i>DF</i>) and the multi-objective manta ray foraging optimizer (MOMRFO), aiming to minimize surface roughness (<i>Ra</i>), cutting force (<i>Ft</i>), and power consumption (<i>Pc</i>). The results indicate that both <i>DF</i> and MOMRFO yield highly effective optimal settings, offering substantial contributions to the domain of hard machining.</p>