The concept of Digital Twin has attracted growing interest within research communities in recent years, including heritage conservation. It combines detailed geometric documentation, real-time monitoring, and semantic information to create dynamic digital replicas of historic buildings. This paper presents the results of a scoping review of 204 peer-reviewed studies published between 2020 and 2025. The aim is to identify the main technologies, devices, and methods used to develop a Digital Twin for heritage buildings. The review reveals that terrestrial laser scanning, UAV photogrammetry, BIM, and IoT sensor networks form the core technological base. It also highlights the growing use of artificial intelligence for automated defect detection, predictive maintenance, and semantic processing. Based on the reviewed literature, the paper introduces a six-stage workflow for building a heritage Digital Twin, covering baseline documentation, static reality capture, semantic modelling, sensor integration, data fusion, and operational use. The findings show a clear shift from static 3D documentation toward dynamic, data-rich systems that support continuous monitoring and more informed decision-making. However, the review also identifies major challenges, including limited interoperability, complex data integration, incomplete AI validation, and long-term digital preservation issues. Overall, the study outlines the current state of Digital Twin technologies in architectural heritage and identifies key areas that require further research to support reliable and sustainable applications.