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In automated manufacturing systems, most of the manufacturing processes, including machining, are automated. Automatic tool change is one of the important parameters for reducing manufacturing lead time. Machining studies on Martensitic Stainless Steel was conducted using Ti[C,N] mixed alumina ceramic cutting tool. Tool life was evaluated using flank wear criterion. The tool life obtained from experimental machining process was taken as training dataset and test dataset for machine learning. Tool life model was developed using Gradient Descent Algorithm. The accuracy of the machine learning model was tested using the test data, and 99.83% accuracy was obtained.