Abrasive finishing process such as grinding is considered one of the most practical means for processing materials for the manufacturing products such as surface finishing, surface quality, and dimension accuracy. Therefore, the grinding machining process is one of the most difficult and least understood processes for two main reasons: first, abrasive grains in grinding tool surface are randomly oriented, and second, the orientation of tool geometry undergoes complex interactions during the time of machining process. The external geometrical interference and vibration during the machining process can affect the tool condition. In this research work, an indirect monitoring method based on sensor fusion and artificial intelligence models has been developed in the past for optimizing and predicting grinding tool wear. In general, monitoring system literature reviews present that have significant differences among them, but there is a matter of fact that there is no clear guidance regarding the implementation of all these techniques. Considering all these factors, this research focuses on a few important issues regarding an intelligent monitoring system: (1) Different sensor fusion models are applied for developing an indirect monitoring system to determine the best sensor combination for the online monitoring process; (2) Most effective signal processing techniques for extracting features from raw data; (3) Selection of important features and extraction method for using relevant sensory information; (4) Developing a design of experiment that is required for modeling a machining monitoring system with a significant amount of data; and (5) Main characteristics of several artificial intelligence techniques to predict the tool condition value with minimum residual.