Metal cutting operations involve intense heat generation owing to plastic deformation of the work piece and due to friction at the tool-work piece and tool-chip interface. The heat generated in metal cutting unfavourably affects the quality and thus the functional performance of the product. It is known that quality and functional performance is the function of roughness and dimensional accuracy. To maintain a longer component life, along with the robust material choice, a component should have good surface finish and dimensional accuracy. While, for the organization to monitor and control their environmental issues in a holistic manner, emphasis in adopting eco-friendly practices and protecting environment has been growing continuously across all the business sectors. In this study, an attempt is made to optimize the process parameter of stainless steel AISI-410 alloy, a nuclear graded material, for better surface finish. For this, Taguchi L9 orthogonal array was utilise to identify the process parameter and cutting environment. Analysis of variance (ANOVA) was also conducted to highlight the significant parameter that affects the surface finish most. A statistical model to forecast the surface roughness was also developed and was validated by an experiment with a maximum error of 12%. Results indicates that feed rate is the most critical factor that effects the surface roughness with the contribution of 91.5%, followed by environment with 5.22% contribution, cutting speed and depth of cut with 2.7 % and 0.4 % respectively. The correlation coefficient of 0.9213 and conformation tests reveals that developed statistical model predicts surface roughness with the statistical error limit.