This review discusses the role of digital twin modeling in enhancing machine tool intelligence. Machine tools, as the backbone of manufacturing, are increasingly becoming intelligent, and digital twin technology is playing a key role in this development. Digital twin modeling is identified as a critical enabling technology for machine tool intelligence, based on a comparative analysis of the characteristics of machine tool intelligence and digital twin technology. The review explores the state-of-the-art digital twin modeling-enabled machine tool intelligence, examining it from the aspects of data-based modeling and mechanism-data dual-driven modeling. It highlights three key challenges in the field. A digital twin machine tool (DTMT) architecture is proposed, and three key technologies are detailed: data perception and fusion technology, mechanism-data-knowledge hybrid-driven digital twin modeling, and virtual-real synchronization technology, as well as dynamic optimization and collaborative control technology for multilevel parameters. Future research directions for the DTMT are also discussed. This work provides a foundation for the research and implementation of digital twin modeling-enabled machine tool intelligence, which is significant for developing intelligent machine tools. Keywords: Machine tool, Digital twin, Smart manufacturing, Synchronization.This review discusses the role of digital twin modeling in enhancing machine tool intelligence. Machine tools, as the backbone of manufacturing, are increasingly becoming intelligent, and digital twin technology is playing a key role in this development. Digital twin modeling is identified as a critical enabling technology for machine tool intelligence, based on a comparative analysis of the characteristics of machine tool intelligence and digital twin technology. The review explores the state-of-the-art digital twin modeling-enabled machine tool intelligence, examining it from the aspects of data-based modeling and mechanism-data dual-driven modeling. It highlights three key challenges in the field. A digital twin machine tool (DTMT) architecture is proposed, and three key technologies are detailed: data perception and fusion technology, mechanism-data-knowledge hybrid-driven digital twin modeling, and virtual-real synchronization technology, as well as dynamic optimization and collaborative control technology for multilevel parameters. Future research directions for the DTMT are also discussed. This work provides a foundation for the research and implementation of digital twin modeling-enabled machine tool intelligence, which is significant for developing intelligent machine tools. Keywords: Machine tool, Digital twin, Smart manufacturing, Synchronization.