<p>Land subsidence, exacerbated by excessive groundwater extraction, has become a critical environmental issue in many urban regions, posing risks to infrastructure stability and sustainable development. In this study, we aim to assess the land subsidence susceptibility of Liaocheng City, China, where rapid urbanization and agricultural demands have led to significant groundwater depletion. We hypothesize that groundwater depth and drawdown are the key drivers of subsidence, particularly in areas with thick clay layers and high extraction rates. To evaluate these factors, we integrate SBAS-InSAR technology with a novel combination of multi-criteria decision-making models, including extension theory and the normal cloud model, alongside subjective (AHP) and objective (CRITIC) weighting methods. Our results reveal distinct spatial patterns of subsidence risk. While most areas exhibit low to moderate susceptibility, Guan County and Xin County show heightened vulnerability, with subsidence exceeding 100 mm over the study period, driven by intensive groundwater extraction and unfavorable geological conditions. In contrast, regions like Dongchangfu District and Gaotang County exhibit low susceptibility, due to effective groundwater management and favorable soil characteristics. The application of extension theory and the normal cloud model provides a refined risk classification, offering detailed insights into the spatial distribution of subsidence risks. These findings emphasize the need for targeted groundwater management policies in high-risk areas and demonstrate the effectiveness of the proposed framework for evaluating subsidence susceptibility. This study contributes a novel, data-driven approach to subsidence risk assessment, with implications for sustainable urban planning and resource management.</p>