<p>The persistent need for manufacturers to innovate and adopt sustainable practices has prompted research into machining with a low carbon footprint, minimal energy consumption by machine tools, and enhanced goods at the lowest cost. In the present work, minimum quantity lubrication (MQL) with graphene powder was employed during the machining of AISI 1045 alloy (used for the fabrication of shafts, connecting rods, axles, etc.). The experimental design follows a Taguchi L₁₆ mixed-level approach, incorporating cutting speed, feed rate, depth of cut, and type of dielectric fluid. The influence of these controllable parameters on the material removal rate (MRR) during turning operations was systematically analyzed. The robustness of the model was confirmed by analysis of variance (ANOVA), showing an <i>R</i>-squared value of 92.32%, demonstrating its predictive reliability. The multi-objective optimization on the basis of ratio analysis (MOORA) was used to rank the experimental alternatives. The obtained results revealed that higher cutting speeds, coupled with nanofluid-enhanced dielectric fluids, lead to superior metal removal rate (MRR) outcomes while reducing machining time and tool tip thermal effects. The study provides a practical framework for optimizing MRR, highlighting the importance of selecting optimal process parameters and fluid compositions for improved efficiency and extended tool life. The findings show that minimum quantity lubrication (MQL) with nanofluids at a 0.35% graphene nanoplatelet (GNP) concentration improves MRR over standard MQL, leading to significant performance enhancements. A comparative analysis of MOORA results indicates a strong correlation between experimental alternatives 8 (MQL) and 8 (MQL mixed GNP). The best compromise for MRR was achieved with MQL + nanofluid graphene at a higher cutting speed (25,500&#xa0;mm/min), greater depth of cut (0.7&#xa0;mm), and low feed rate (0.2&#xa0;mm/rev).</p>