To address the limitations of conventional individual weighting methods and subjective selection uncertainties in water resources carrying capacity assessment, a comprehensive evaluation indicator system was established. In consideration of the inherent uncertainties in water resources carrying capacity evaluation, an innovative methodology integrating game theory was developed to optimize individual weighting methods for indicator weighting determination. The improved grey correlation-TOPSIS method was employed to calculate closeness degree, coupled with an enhanced cloud model algorithm. This proposed game theory-cloud model hybrid approach was applied to assess the spatiotemporal evolution of water resources carrying capacity in eight cities within the middle-lower reaches of the Hanjiang River in Hubei Province from 2010 to 2020. The results indicate that the water resources carrying capacity was higher in the upstream areas compared to the downstream areas, exhibiting strong clustering characteristics and significant spatial heterogeneity. The water resources carrying capacity was at its lowest level in 2011 and reached its highest level in 2020, showing an overall fluctuating upward trend. The water resources carrying capacity of each city was primarily at a moderate level, with occasional alert-level occurring in specific years. Keywords: water resources carrying capacity; game theory; cloud model; middle-lower reaches of the Hanjiang River; Hubei Province 〖FL