The fundamental property of a material that determines productivity changes, machining costs, and material selection optimization in the design of mechanical parts is the machinability rating of engineering material. As a result, this work focuses on the study of surface roughness parameter (Ra), tool temperature (Tt), and work temperature (Tw) while performing Lathe on the Ti alloy, which is a relatively low machinability alloy, for various combinations of machining parameters like feed (f), depth of cut (doc), and spindle speed (N) under wet conditions using Taguchi philosophy with non-coated PVD tool inserts. A surface roughness tester and an infrared gun are used to measure roughness and temperature. For experimental design, Taguchi design of experiments (DOE) based on Orthogonal Arrays (OA) and signal-to-noise ratio (S/N ratio) is used. The generated responses are used to predict the performance and significance of machining parameter combinations in CNC lathe operations using Analysis of Variance (ANOVA). Individual response optimization is carried out using S/N ratios of the responses, and multi-response optimization is carried out using Grey Relational Analysis (GRA) for both machining conditions and better responses in wet conditions.