Digital twin governance, leveraging advanced technologies, plays a pivotal role in the systematic and sustainable development of smart cities by enhancing resource management and optimizing urban planning. Accordingly, this study aims to examine urban digital twin governance in the systematic development of Tehran’s smart city. The research follows a descriptive–analytical method, integrating a meta-analysis, systematic review, and path analysis approach. In the systematic review phase, 305 selected articles were extracted from an initial pool of 6,226 publications (2010–2025) using the Scopus database and refined through the PRISMA protocol, followed by bibliometric analysis via VOSviewer. The research variables were identified based on systematic review outputs, and data were analyzed using Structural Equation Modeling (PLS3). The statistical population included experts and managers involved in smart city and urban digital twin governance, with a purposive sample of 96 participants. The systematic review results revealed that data management, data security, and urban modeling have the greatest influence on the success of digital twin governance, while challenges such as institutional misalignment, lack of data-driven infrastructure, and absence of transparent data regulations persist. The structural modeling findings indicated that data management and modeling are crucial for enhancing decision-making and urban sustainability. Urban digital twin governance provides the necessary institutional framework for coordinating actions, although issues of data security and integration remain significant. Therefore, transitioning toward an efficient digital twin ecosystem requires institutional integration, citizen participation, and the use of artificial intelligence (AI), Internet of Things (IoT), and cloud computing technologies—factors that could collectively strengthen Tehran’s urban sustainability in the future.