This study explores the potential of a hybrid artificial intelligence (AI) approach for optimizing the turning parameters of D2 steel. The research investigates the influence of textured cutting tools on achieving optimal machining performance while prioritizing eco-friendly Minimum Quantity Lubrication (MQL) practices. A novel approach is proposed, combining Grey Relational Analysis (GRA) for multi-objective evaluation, the Adaptive Neuro-Fuzzy Inference System (ANFIS) for robust modeling, and the JAYA algorithm for efficient optimization. GRA transforms multi-objective responses, including cutting zone temperature, surface roughness, tool wear, and material removal rate (MRR), into a single Grey Relational Grade (GRG) under MQL conditions. This GRG represents the desired machining efficiency that balances performance and environmental impact. ANFIS, leveraging the strengths of both neural networks and fuzzy logic, models the relationship between control parameters (speed, feed, depth of cut, and cutting tool type like untextured or textured) and the MQL-based GRG. The JAYA algorithm then optimizes these control parameters to achieve the highest GRG, signifying minimal cutting zone temperature, surface roughness, and tool wear, maximizing MRR within the MQL environment. Experimental turning trials validate the effectiveness of the proposed approach. This research offers a significant contribution to the automotive industry. By incorporating textured tools, utilizing MQL, and employing the GRA-ANFIS-JAYA framework, the approach facilitates exploration of improved surface integrity, potentially leading to enhanced component performance and lifespan. Additionally, optimizing for minimal tool wear under MQL translates to cost savings and reduced environmental impact. This research paves the way for sustainable and efficient machining practices in the automotive industry.