This survey paper provides a comprehensive analysis of Digital Twin (DT) technology in Smart Cities, based on a bibliometric study of over 4,220 articles from the Web of Science. The study explores the integration of DT with Machine Learning, IoT, and its role in Smart City development. It identifies key trends, themes, and challenges in DT research, highlighting the importance of DT in urban applications such as urban planning, energy management, traffic control, environmental monitoring, public safety, and healthcare. The paper also discusses the challenges and limitations of DT implementation, including data integration, scalability, and security concerns. It presents case studies showcasing successful DT applications in Smart Cities and outlines future research directions. The study employs a systematic approach to analyze the literature, using tools like VOSviewer for data visualization. Key findings include publication trends, prolific authors, and thematic clusters in research. The paper emphasizes the evolving role of policy and governance in DT development and provides a detailed overview of DT definitions, concepts, architecture, enabling technologies, applications, challenges, performance metrics, datasets, software, and use cases. The study contributes to the field by offering a comprehensive and systematic bibliometric analysis of DT technology and Smart Cities, highlighting the significance of IoT and Machine Learning in DT applications. The paper also discusses the geographical distribution of research, source co-citation, and international collaboration in DT research. It outlines the applications of DT in Smart Cities, including urban planning and management, energy management, traffic and mobility management, environmental monitoring and management, public health and safety, and citizen-centric aspects. The study concludes that DT technology plays a crucial role in transforming Smart Cities into more efficient, sustainable, and responsive environments.This survey paper provides a comprehensive analysis of Digital Twin (DT) technology in Smart Cities, based on a bibliometric study of over 4,220 articles from the Web of Science. The study explores the integration of DT with Machine Learning, IoT, and its role in Smart City development. It identifies key trends, themes, and challenges in DT research, highlighting the importance of DT in urban applications such as urban planning, energy management, traffic control, environmental monitoring, public safety, and healthcare. The paper also discusses the challenges and limitations of DT implementation, including data integration, scalability, and security concerns. It presents case studies showcasing successful DT applications in Smart Cities and outlines future research directions. The study employs a systematic approach to analyze the literature, using tools like VOSviewer for data visualization. Key findings include publication trends, prolific authors, and thematic clusters in research. The paper emphasizes the evolving role of policy and governance in DT development and provides a detailed overview of DT definitions, concepts, architecture, enabling technologies, applications, challenges, performance metrics, datasets, software, and use cases. The study contributes to the field by offering a comprehensive and systematic bibliometric analysis of DT technology and Smart Cities, highlighting the significance of IoT and Machine Learning in DT applications. The paper also discusses the geographical distribution of research, source co-citation, and international collaboration in DT research. It outlines the applications of DT in Smart Cities, including urban planning and management, energy management, traffic and mobility management, environmental monitoring and management, public health and safety, and citizen-centric aspects. The study concludes that DT technology plays a crucial role in transforming Smart Cities into more efficient, sustainable, and responsive environments.