Abstract: Advancing the intelligent development of mines is a crucial pathway to enhance the safety, efficiency, and green development of the coal industry. As a new-generation information technology, digital twin technology provides important support for the intelligent transformation in the field of fully-mechanized coal mining equipment. Therefore, a review study on the digital twin technology of fully-mechanized coal mining equipment was carried out. First, the fundamental concepts and core principles of digital twins are elucidated. Building upon this foundation, the review focuses on the current application landscape within the mechanical engineering sector. It specifically analyzes the practical implementation of digital twin technology in mechanical design and manufacturing, fault diagnosis and maintenance, as well as supply chain collaboration, providing theoretical and technical references for its application in the coal mining field. Subsequently, the review examines the current applications and future development trends of digital twin technology in coal mining, with particular emphasis on the technical aspects of fully-mechanized mining equipment. It provides a detailed overview of the research status regarding the integration of digital twins with three fully-mechanized mining machines and intelligent fully-mechanized mining systems, covering specific application scenarios such as condition monitoring, fault prediction, adaptive control, intelligent cutting, and collaborative operation and maintenance. Research indicates that digital twin technology enables full lifecycle management of critical equipment—including shearers, scraper conveyors, and hydraulic supports—through real-time data acquisition, virtual modeling, and intelligent analysis, significantly enhancing fault prediction accuracy and production efficiency. Looking ahead, breakthroughs are needed in key technical bottlenecks such as multi-physics coupling modeling, high-precision underground sensing, and cross-system collaborative decision-making. This will drive a fundamental shift in research paradigms from “model/ data-driven” to “fusion-driven”, and from “single-device optimization” to “system control coordination”. By establishing industry standards and building open-source ecosystems, we can overcome data barriers and interoperability challenges, thereby promoting the deep application and effective implementation of digital twins in smart mine construction and intelligent unmanned mining.