Urban flood prevention faces challenges in real-time monitoring, data integration, and decision-making support, particularly under constraints of sensitive data security and efficient emergency response. This study proposes an artificial intelligence-assisted decision-making framework for urban flood prevention, integrating multi-dimensional data fusion via an intranet. A city-level digital twin platform is developed, combining real-time sensor data, meteorological information, and social media data using a geographic information system, the Internet of Things, and artificial intelligence algorithms. The framework ensures secure data processing through intranet-based operations, enhancing flood prediction accuracy and emergency response efficiency. The system was successfully implemented in Hebi City, Henan Province, demonstrating significant improvements in flood risk management. This research advances urban resilience and provides a scientific basis for flood management in smart cities.