The growing complexity of urban challenges in the 21st century has spurred the need for innovative, data-driven tools in urban planning. One such emerging tool is the digital twin—a virtual replica of physical city systems that integrates real-time data, simulation models, and advanced analytics to support sustainable and resilient urban development. This paper conducts a systematic literature review (SLR) and thematic analysis of 548 recent studies to explore the methodologies and technological frameworks used to develop city digital twins. The analysis identifies two major themes: (1) models, frameworks, and tools for digital twin development and (2) data management strategies for effective implementation. By categorizing technological integrations such as GIS, BIM, remote sensing, machine learning, and IoT, this review highlights best practices and challenges in current urban digital twin applications. The findings suggest that while digital twins hold significant potential to advance Sustainable Development Goals (SDGs), gaps remain in terms of standardization, governance, interoperability, and bi-directional data flow. This study concludes with a call for more empirical research on bidirectional integration between the digital replica and its physical counterpart, particularly in developing countries, to unlock the full transformative potential of digital twins in smart urban governance.