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A 2024 climate change study in Taiwan indicated an increase in rainfall of approximately 10-35%, causing flooding in some urban areas where stormwater drainage systems exceeded their original design protection standards. Furthermore, urban stormwater drainage systems improvements in Taiwan often face complex and intertwined spatial issues related to road traffic and underground utility lines, making rapid engineering improvements difficult. Therefore, to address the threats already posed by climate change, the use of big data monitoring of urban areas and surrounding regions, along with rapid AI-powered algorithms for drainage systems, is imperative. The New Taipei City Government, in order to manage urban water information, has developed a series of adaptation strategies for its drainage system. These strategies address environmental factors such as drainage sections affected by tides and storm surges, rainfall characteristics in nearby mountainous areas, and sections with gates and pumping stations that cannot drain by gravity. The aim is to lower urban drainage levels to prevent flooding and shorten flooding duration. This includes practical operational recommendations and early flood warnings. The method is based on historical practical experience and AI-generated water level forecasts to conduct drainage system decision analysis and management value setting. It combines real-time rainfall data from the Internet of Things, road flooding sensors, road CCTV, stormwater sewer water levels, and pumping station water levels. The data used includes actual data from the past 3 hours, forecasted rainfall for the next 6 hours, tidal changes, and real-time water level information at various monitoring locations to formulate adjustment strategies. Synchronous information is released within the drainage system to systematically set stormwater sewer water levels, treating stormwater sewers as flood retention spaces for monitoring and water level control. Based on operational experience gained from the past 3 years of implementation, this method will be used in the future to address the threats posed by increased rainfall due to climate change and to formulate urban flood control strategies to reduce disaster losses.