Hybrid AI-IoT Framework with Digital Twin Integration for Predictive Urban Infrastructure Management in Smart Cities

The evolution of cities into digitally managed environments requires computational systems that can operate in real time while supporting predictive and adaptive infrastructure management. Earlier approaches have often advanced one dimension—such as Internet of Things (IoT)-based data acquisition, Artificial Intelligence (AI)-driven analytics, or digital twin visualization—without fully integrating these strands into a single operational loop. As a result, many existing solutions encounter bottlenecks in responsiveness, interoperability, and scalability, while also leaving concerns about data privacy unresolved. This research introduces a hybrid AI–IoT–Digital Twin framework that combines continuous sensing, distributed intelligence, and simulation-based decision support. The design incorporates multi-source sensor data, lightweight edge inference through Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) models, and federated learning enhanced with secure aggregation and differential privacy to maintain confidentiality. A digital twin layer extends these capabilities by simulating city assets such as traffic flows and water networks, generating what-if scenarios, and issuing actionable control signals. Complementary modules, including model compression and synchronization protocols, are embedded to ensure reliability in bandwidth-constrained and heterogeneous urban environments. The framework is validated in two urban domains: traffic management, where it adapts signal cycles based on real-time congestion patterns, and pipeline monitoring, where it anticipates leaks through pressure and vibration data. Experimental results show a 28% reduction in response time, a 35% decrease in maintenance costs, and a marked reduction in false positives relative to conventional baselines. The architecture also demonstrates stability across 50+ edge devices under federated training and resilience to uneven node participation. The proposed system provides a scalable and privacy-aware foundation for predictive urban infrastructure management. By closing the loop between sensing, learning, and control, it reduces operator dependence, enhances resource efficiency, and supports transparent governance models for emerging smart cities.