[Objective] The temporal and spatial variation of understory vegetation coverage and its driving factors in Hubei Province were studied to provide scientific basis for ecological environment protection and vegetation management in this area. [Methods] Hubei Province was selected as the study area, and understory green leaf vegetation cover and understory litter cover were extracted from 28 sample plots for every half month in 2022 through field survey and DeepLabV3⁺ semantic segmentation method. Based on this, various machine learning models were used to analyze the impact of four driving factors, namely spatial location, natural environment, social and economic environment, and climate conditions, on changes in understory green leaf vegetation cover. [Results] Monthly variations in understory litter cover did not show obvious seasonal characteristics, and there was considerable spatial and temporal heterogeneity in the understory litter cover among the sample plots. In contrast, understory green leaf vegetation cover showed obvious seasonal change characteristics, and there were significant differences in understory green leaf vegetation cover among different vegetation types; the understory green leaf vegetation cover of economic forests and conifers was generally higher than that of broadleaf deciduous forests and evergreen broadleaf forests, and the differences in understory green leaf vegetation cover between broadleaf deciduous and evergreen broadleaf forests were relatively small. The random forest regression model showed the best prediction performance, with a root mean square error (RMSE) of 11.072 3 and a coefficient of determination (R²) of 0.732. [Conclusion] The random forest regression model showed that temperature, NDVI, and precipitation in the previous month were the key driving factors for spatiotemporal changes in the understory green leaf vegetation cover.