The current research article develops artificial neural network (ANN) and regression analysis for prediction and optimization of flank wear. For this, the flank wear results were observed from live experiments conducted under the various levels of input variables like speed, feed and depth of cut. The ANN model architecture: 9-4-1 was observed as suitable for this analysis. The experimental results were utilized to train, test and validate the network. Neural intelligence tool was used to perform ANN analysis. The prediction capability of the regression model was estimated based on R-value (correlation coefficient) among experiment’s and model prediction’s values. The predictions by ANN were found to be in precise among the predictive models as obtained from the R: squared value and correlation value. The insert life was also evaluated with respect different machining time at various level of machining factors. The flank wear, which is equal to equal to 0.35 mm, was considered as criteria to decide the life of the insert. Further, the machining factors were optimized based on desirability approach. Therefore, these predictive models and optimized factors would pave the aero industries to predict as well as optimize the flank wear. The result of the experiments has revealed the feed rate as noteworthy variable than speed and depth of cu

Keywords: Monel K500, Taguchi, ANN, Flank wear, Tool life