Machining through turning is very critical when it comes to the creation of cylindrical components that exhibit superior surface finish and precision. To improve on the performance of machining while at the same time increasing efficiency and reducing costs, the appropriate cutting parameters have to be considered. Multi-objective optimization methods have become increasingly popular in recent times due to the fact that machining performance depends on several objectives that compete with each other, including surface roughness minimization and maximum material removal rate. This paper discusses the use of Taguchi method in combination with Grey Relational Analysis (GRA) as a multi-objective optimization process in turning. Parameters considered for performance include Ra (surface roughness), MRR (material removal rate), and VB (tool wear). The results obtained from experiments were analyzed using Grey Relational Analysis to find out the effect of various parameters on turning operation performance by combining various responses in terms of one Grey Relational Grade (GRG). The significance of the parameters used in the machining process was determined by analysis of variance (ANOVA). Results revealed that the most significant factor affecting performance is cutting speed. An increase in productivity and quality of the machined surface was achieved simultaneously using optimal parameter values.