In this paper, developing a reliable mathematical model for cooling efficiency representing machining conditions of a particular work-tool combination, using near dry lubrication. of solid–liquid in various proportions is used, keeping in view environmentally conscious manufacturing. The range of each machining parameter were selected at three different levels, like low, middle, and high based on industrial application. Five controllable parameters during the machining of alloy steel have been studied. Statistical design of experiment (DOE) technique is used in this research work for getting accurate and scientific results during machining of alloy steel. Factorial design with eight added centre points (25 + 8) is a composite design used in this research paper. The relationship between the cooling efficiency and cutting conditions were analyzed. In the development of predictive model, cutting conditions and tool geometry (tool nose radius) and various proportions of solid –liquid lubricant were considered as model variables and cooling efficiency considered as response variable. Using the experimental data of cooling efficiency, a regression analysis with logarithmic transformation correlation model has been developed to predict the cooling efficiency during machining of alloy steel. The experimental data collected during machining analyzed by statistically through analysis of variance technique. Rotating vector operator process (ROVOP) optimization method is used for optimization of machining parameters.