AI-DRIVEN SUSTAINABLE INFRASTRUCTURE FOR SMART CITY DEVELOPMENT

Rapid urbanization has increased pressure on transportation, energy, water, and waste infrastructure, creating the need for intelligent and sustainable urban management systems. This study proposes an AI-driven sustainable infrastructure framework for smart city development by integrating IoT sensors, GIS intelligence, machine learning, and digital twin simulation. Using secondary urban datasets from traffic systems, energy meters, water networks, and environmental monitoring stations, the model enables predictive analytics and real-time decision support. Advanced AI techniques such as Random Forest, XGBoost, and LSTM are applied to optimize traffic flow, predict infrastructure failures, reduce emissions, and strengthen disaster preparedness. The findings show significant improvements in mobility efficiency, energy optimization, governance transparency, and citizen service delivery. Strongly aligned with SDG 11 and India’s Smart Cities Mission, the framework offers a scalable roadmap for tier-2 cities such as Bhubaneswar, supporting resilient, data-driven, and sustainable urban infrastructure governance.