Milling is one of the most versatile processes used in the manufacture of various components. With this, the milling tool usage has gained momentum, so as the research on its wear phenomenon. Flank wear has been considered as, one of the most commonly observed and an unavoidable phenomenon in metal cutting process, which is also a major source of economic loss resulting due to material loss and machine downtime. With the aim of implementing a predictive maintenance for the milling process, so as to avoid unnecessary cost and wastage of time due to sudden failure of cutting tool, and also to maintain the best product output quality, one of the applications of Machine Learning has been presented in this thesis by giving due importance to Tool Condition Monitoring.