The rapid expansion of urban populations has intensified the need for efficient, resilient, and sustainable infrastructure systems. Conventional approaches to urban planning and infrastructure management often rely on static datasets and fragmented analytical methods, which limit their responsiveness to real-time challenges. In recent years, digital twin technology has emerged as a transformative paradigm that enables the creation of dynamic, data-driven virtual representations of physical systems. These virtual models are continuously updated through real-time data streams obtained from sensors, Internet of Things (IoT) devices, and other digital sources. This paper examines the role of digital twins in advancing smart urban infrastructure. It explores their applications across transportation, energy systems, environmental monitoring, and governance frameworks. A structured review of recent journal literature is conducted, followed by the development of a conceptual implementation framework. The analysis reveals that digital twins significantly improve operational efficiency, predictive maintenance capabilities, and decision-making processes. However, challenges such as data standardization, integration complexity, and cybersecurity risks remain critical barriers. The study concludes that digital twins serve as a foundational technology for smart cities, enabling proactive and sustainable urban development through integrated digital ecosystems.