Next-Generation Infrastructure: Digital Twins, IoT, and AI for Urban Planning

Rapid urbanization and infrastructure pressures have underscored the need for adaptive, data-driven urban planning systems. This study proposes a next-generation urban infrastructure framework that integrates Digital Twins, the Internet of Things (IoT), and Artificial Intelligence (AI) to enable real-time monitoring, predictive analysis, and intelligent decision-making. A comprehensive conceptual analysis establishes the role of digital replicas, sensor networks, and learning algorithms in forming cyber-physical urban ecosystems. Building on this foundation, a framework analysis is developed to align physical assets, digital models, and data streams through interoperable architectures. The problem addressed is the persistent gap between traditional, static urban planning approaches and the dynamic, responsive infrastructure required for sustainable cities. A mixed-method research design is employed, combining computational modeling with quantitative data analysis derived from urban sensor networks. Key results indicate significant improvements in predictive accuracy, infrastructure resilience, and operational efficiency through the proposed framework. The impact of this research lies in advancing smart city planning methodologies, informing policy for sustainable urban development, and contributing a scalable model for real-time, AI-driven infrastructure management.