REVIEW PAPER Journal of The Institution of Engineers (India): Series C https://doi.org/10.1007/s40032-024-01118-z was made possible by the realization that AI and ML could greatly improve machining processes by using data-driven insights to monitor tool wear, predict cutting forces, opti- mize machining parameters, and improve surface quality in machined components [4]. Additionally, implementing ML systems has the potential to improve cutting tool longev- ity, increase productivity, and lower energy usage, all of which are in line with sustainability goals. Computer con- trol, information technologies and various sensors are major requirements for the machining industry [5, 6]. The rapid advancement of fast, precise, and AI-driven applications has signiﬁcantly reduced the need for human intervention. Spe- ciﬁcally, ML has made signiﬁcant progress in the machin- ing industry, demonstrating its ability to handle complex challenges such as time-dependent dynamics [7]. Figure 1 illustrates the Industry 4.0 and 5.0 manufacturing pyramid. The ability of ML to model and predict complex interac- tions between simulation and experimental data is a major factor in its success in this area. The increasing autonomy of machining systems underscores the value of ML applica- tions. Furthermore, the extensive integration of sensors in industry results in the generation of vast amounts of data, providing an opportunity to employ intelligent systems to Introduction AI and ML are being implemented in machining operations to increase precision, eﬃciency, accuracy, and productivity [1]. The need for increased quality has pushed the transi- tion of machining toward being smart, cost reduction, digi- tal, and autonomous [2]. The incorporation of AI and ML into machining operations is a signiﬁcant turning point in this direction and has the potential to completely transform conventional manufacturing paradigms [3]. This integration  Nitin Ambhore nitin.ambhore@vit.edu 1 Department of Mechanical Engineering, Shri Vishnu Engineering College for Women, Bhimavaram 534202, India 2 Department of Mechanical Engineering, Vishwakarma Institute of Information Technology, Pune 411048, India 3 Department of Mechanical Engineering, CMR College of Engineering & Technology, Hyderabad, Telangana 501401, India 4 Department of Mechanical Engineering, Vishwakarma Institute of Technology, Pune 411037, India Abstract Industry 4.0 and 5.0 have led to the extensive implementation of Artiﬁcial Intelligence (AI) and Machine Learning (ML). AI and ML signify a signiﬁcant breakthrough in numerous ﬁelds by enabling more eﬃcient data processing, oﬀering enhancements across various services, and automation to replicate the learning process of machines, thereby enhancing system accuracy. In machining processes, AI and ML play crucial roles in predicting cutting forces, tool wear, and opti- mizing machining parameters. By employing advanced ML systems, machining operations can achieve longer cutting tool lifespan and increased eﬃciency. Additionally, these systems enable the prediction and enhancement of surface quality in machined components, contributing to overall part quality improvement. Furthermore, ML techniques are instrumental in analyzing and reducing power consumption during machining operations by predicting the energy consumption patterns of machine tools. This paper reviews the applications of AI and ML in machining operations and suggests future research directions. By examining recent achievements in the available literature, it aims to advance the research ﬁeld by oﬀering innovative concepts and approaches for integrating AI and ML into machining industries. Keywords Artiﬁcial intelligence · Machine learning · Machining operations · Industry 4.0 and 5.0 Received: 19 July 2024 / Accepted: 8 October 2024 © The Institution of Engineers (India) 2024 Machine Learning and Artiﬁcial Intelligence Supported Machining: A Review and Insights for Future Research Javvadi Eswara Manikanta 1 · Nitin Ambhore 4 · Amol Dhumal 2 · Naveen Kumar Gurajala 3 · Ganesh Narkhede 2 1 3