Stormwater management is a crucial environmental and public health issue, particularly in urban areas, where impervious surfaces limit rainwater infiltration into the soil. Traditional stormwater management systems have limitations in detecting and predicting the behavior of runoff, which can lead to flooding. This study focuses on using the Internet of Things (IoT) and Artificial Intelligence (AI) technologies to optimize stormwater systems. Overall, these systems employ various IoT devices to monitor critical parameters of the stormwater network in real-time. The data collected by these devices are processed and analyzed using AI algorithms such as machine learning and deep learning, which enable the system to detect anomalies, predict potential flooding events, and provide decision-makers with valuable insights into the behavior of surface runoff. Integrating IoT and AI technologies in stormwater man-agement improves efficiency, early issues detection, real-time decision-making, and reduced environmental damage. Thus, this study shows a possible architecture that exploits IoT and AI technologies to build a stormwater management system that can provide real-time and predictive control and monitoring.