Generative AI for Smart City Management: A Review of Large Language Models, Synthetic Data, Scenario Simulation and Urban Digital Twins

Rapid urbanization places pressure on traffic, energy, water, waste, environment and public services, and traditional city management often struggles with congestion, pollution and inefficient use of resources. Generative Artificial Intelligence (GenAI), comprising models that create text, images, time series and simulations, offers new tools for city administrators, planners and citizens. This paper reviews the application of GenAI to smart city management. It examines foundation models and large language models that provide conversational access to city data and assist with reports and citizen services; generative models that synthesize realistic sensor data and future scenarios; graph-based spatio-temporal models for traffic forecasting that feed generative planning; and urban digital twins in which generated scenarios can be tested before decisions are made. Applications in traffic, energy, environmental monitoring, waste management, public safety and urban planning are discussed. A five-layer architecture integrating IoT data, a city data platform, predictive and generative AI, a digital twin and stakeholder interfaces is proposed, with research gaps, objectives, a comparison of AI roles and challenges including hallucination, bias, data governance, privacy and accountability. The review concludes that GenAI can make city management more efficient and participatory when grounded in reliable data and human oversight.