With the advent of emerging technologies, there is a growing demand to enhance overall efficiency with respect to energy, productivity, and tooling. As a result, there is a focus on making machining processes simultaneously sustainable, productive, and efficient. In this research, the influence of machining parameters in CNC lathe, such as feed, cutting speed, and depth of cut, were evaluated under both dry and wet conditions. Since sustainable production requires a balance between energy consumption and quality, response variables like Ra and MRR were analysed. A Taguchi-grey integrated approach was adopted. For single-response analysis, Analysis of Variance (ANOVA), main effect plots, and response tables were employed. The grey relational methodology was used for multiobjective optimization. The ANOVA results indicated that depth of cut had the greatest impact on surface roughness and MRR, followed by feed in Study 1. In Study 2, under wet conditions, feed emerged as the most significant factor affecting surface roughness, while cutting speed was the primary contributor to MRR. According to the grey relational analysis, the optimal combination for realizing the best MRR and Ra in both environments was a cutting speed of 700 rpm, a feed rate of 0.18 mm/rev, and a depth of cut of 1 mm.