Climate-induced shifts in the composition and structure of alpine vegetation cover, both expansion and reduction, are altering alpine ecosystem functions. However, accurately quantifying variations over large-scale regions requires a detailed characterization of the fine-scale mosaic vegetation covers. In this study, we employed a regression-based unmixing model using synthetic data to develop a multi-temporal machine learning model aimed to estimate the fractions of alpine plant functional types (PFTs) from 1984 to 2024 in the Yarlung Zangbo River Basin (YZRB), China. The estimated cover fractions for tree cover, shrub cover and herbaceous cover had mean absolute errors of 10.36%, 14.06% and 13.38%, respectively. The variations in the fractions of each alpine PFT revealed a slight increase in tree cover and shrub cover, alongside a contraction in herbaceous cover. Specifically, tree cover and shrub cover expanded by +1.54% and +1.83% per decade, respectively, while herbaceous cover declined at a rate of 1.98% per decade. These variations were predominantly observed at higher elevations (4000–6000 m), on shaded aspects, and on lower slopes. The variations in these fractions are also positively correlated with air temperature and soil moisture in most regions. This study provides new insights into vegetation cover shifts in this ecologically sensitive region.