With the intensification of global climate change and ecological degradation, the protection and sustainable management of vegetation resources in Sichuan Province have become critical research areas. This paper, leveraging multi-source information fusion technology, integrates remote sensing data, Geographic Information System (GIS) data, meteorological data, and ground observation data to propose a high-resolution spatiotemporal prediction model for vegetation resources across the province. Using an XGBoost algorithm combined with high-precision spatial grid data, the study accurately predicts the distribution of vegetation resources and provides an in-depth analysis of the impact of urbanization on vegetation cover across various cities in Sichuan. For example, plateau areas such as Ganzi Prefecture (MEAN = 68.03, STD = 12.23) and Aba Prefecture (MEAN = 49.81, STD = 10.93) exhibit rich and uniform vegetation cover. In contrast, urbanized regions like Chengdu (MEAN = 2 6.18, S TD = 21.77) s how s ignificantly l ower v egetation c overage, a lthough t he s uburban areas around Chengdu still maintain considerable natural resource richness. The model achieved an RMSE of 8.7 and an R² of 0.82, demonstrating high accuracy and robustness. The results offer crucial insights for improving ecological management and promoting sustainable development in Sichuan Province while also serving as a technical foundation for environmental protection in other regions with similar ecological challenges.