The current trend of Industry 4.0 development requires an integration of the process sensing technologies with the cloud computing network to design the cyber-physical systems which can seamlessly transfer data between the connected devices. This will lead to real-time process monitoring as well as the control of the process by the use of decision-making algorithms. This seminar describes the Kalman filter based tool wear prediction approach adopted during end milling of Ti-6Al-4V alloy at various operating conditions. A series of slot milling passes were made at various parameter combinations of feed, speed, and depth of cut until the flank wear on tool crosses the failure criterion. The cutting force data acquired during the process with the dynamometer and the texture features from the image of the milled surface are used to build a model for predicting the flank wear using the Kalman filter approach. The Kalman filter is an estimator which is stochastic in nature. It integrates the feedback from predicted values to improve the estimates. The fusion model built using the Kalman filter methodology achieves a good accuracy in predicting the flank wear on the tool. This model is highly accurate in predicting the wear as the tool approaches the failure threshold. Thus, the developed model can enable the decision control module to trigger a tool change signal before failure and improve the overall productivity of the process.