Abstract: As the core equipment of modern manufacturing, the digital transformation of CNC machine tools has become a key driver for high-quality development of the manufacturing industry. This paper reviews the application status and development trends of digital engineering throughout the lifecycle of CNC machine tools, encompassing design, manufacturing, and operation and maintenance. It specifically focuses on the synergistic logic, technical characteristics, and engineering practices of four key technologies: data mining, digital twins, machine learning, and generative artificial intelligence. Research demonstrates that data mining technology facilitates data-driven optimization of manufacturing processes, enabling the structured accumulation of implicit experience. Digital twins provide a high-precision simulation environment through virtual-physical interaction, enhancing machine tool performance optimization and intelligent operation and maintenance. Machine learning accelerates multi-physics-coupled surrogate modeling and intelligent decision-making, while generative artificial intelligence overcomes the bottleneck of data scarcity and drives innovative design. Current research still faces challenges in heterogeneous data integration, model generality, and real-time performance. Future efforts should focus on constructing a "process-physics" hybrid-driven framework and strengthening a multi-domain-integrated digital ecosystem to achieve autonomous optimization and intelligent upgrades throughout the lifecycle of machine tools.