As the Internet of Things (IoT) matures, large-scale groundwater flow data collection, transmission, and storage can be carried out through many sensors. There are significant resource requirements for data analysis and data storage platforms. Cloud computing has been the latest computing management system developed in recent years. It shows exponential growth in its application to groundwater modeling systems and subsidence monitoring work. The goal of this study is to apply cloud computing and IoT technologies to continuously collect real-time groundwater-related data and transmit it to a cloud platform. We developed a hydrological and subsidence monitoring platform that can process large-scale groundwater data and use database systems to store the data and ensure data integrity. Therefore, the efficiency of execution is discussed when data processing and storage are performed. The platform can monitor the state of the groundwater layer in the Choshui River Alluvial Fan in Taiwan. The platform displays the data of the groundwater layer visually, clearly understanding the situation of land subsidence in the Choshui River Alluvial Fan in Taiwan and providing managers, researchers, and users with accurate quantitative assessment methods.