In the machining process, tool wear is an unavoidable reason for tool failure. Tool wear has an impact on not just tool life but also the quality of the finished product in terms of dimensional accuracy and surface integrity. Tool wear is a significant element in the annual cost of machining. It happens when the tool-work contact zone experiences abrupt geometrical damage, frictional force, and heat generation. It's essential to accurately evaluate tool wear during machining so that the cutting tool can be replaced before the workpiece surface sustains significant damage. The capacity to assess tool wear is crucial for ensuring high-quality workpieces. Artificial neural network, Deep learning and Machine learning systems, heat generation analysis, image data processing, finite element method and gaussian process are used in order to accurately predict the tool wear during machining operations. In this paper, cutting tool wear prediction in machining operations is reviewed in order to be analyzed and minimized. The main purpose of the study is to provide a useful resource for researchers in the field by presenting an overview of current research on cutting tool wear prediction in machining processes. As a consequence, the research area can be progressed by reading and assessing existing achievements in published articles in order to provide new ideas and methodologies in prediction and minimization of tool wear during machining operations.