Today, digitalization is a "must" for all industries and sectors. One of the most promising and popular concepts of digitalization in the last decade is certainly the technology of the digital twin (DT). As rail transport ranks very high among transport modes in terms of sustainability, resilience and reliability, its digitalization is of great importance. Since any disruption or interruption of normal rail operations with the aim of improving conditions could have many negative consequences, the technology of DT is perfect in this sense, as it allows testing changes on a virtual copy of the real system without directly affecting its operation. Combined with machine learning and other artificial intelligence algorithms, it can also be used to predict and forecast operational characteristics, events and failures that can trigger rapid management and action and prevent disruptions and cost increases. This article, therefore, aims firstly to provide a comprehensive and overarching overview of the application of DT technology in the rail sector using a newly proposed multi-layered classification, and secondly to highlight the research gaps that need to be addressed in the near future to enable the transition to Rail 4.0. The result of this research, which examined 58 research articles published in scientific journals, shows that the application of DT in the railway sector has increased in recent years, that investigations in infrastructure for maintenance purposes receive the most attention in the scientific community, and that there is still much room for research and development of DT in the railway sector.