Digital twin-driven machining and optimizing process parameters is a promising approach for improving the efficiency and quality of machining processes. This approach leverages digital twins (DTs), virtual models of physical systems that enable real-time monitoring and simulation of system behaviour. Manufacturers can reduce waste, improve productivity, and enhance product quality by using digital twins to monitor and optimize machining processes. This article reviews recent studies on digital twin-driven machining and process parameter optimization, highlighting this approach’s benefits, challenges, and limitations. The future research directions in this field include developing more advanced digital twin models and algorithms, integrating with other technologies such as artificial intelligence and machine learning, and applications in emerging fields such as additive manufacturing and smart factories. Gradually becoming a contested area in smart machining, digital twin (DT) technology enables the quality control of the dynamic cutting process by constructing the allegiance DT models. There is a shortage of detailed and systematic studies of DT-driven machining despite certain review publications focussing on digital twins (DTs). This study begins by introducing the Digital Twin technology for machining systems. Then, the DT-driven machining system’s essential features, procedures, and services are analysed. Future study areas are also suggested based on the analysis of DT-driven machining. The article discusses the potential of digital twin-driven machining and process parameter optimization in improving manufacturing efficiency and quality, emphasizes its significance, and highlights the need for further comprehensive research.