Active layer moisture (ALM) plays a fundamental role in regulating the hydrothermal dynamics, freeze–thaw processes, carbon cycling, and ecosystem and load-bearing functions of permafrost. However, spatially continuous, high-resolution datasets that characterize soil moisture across the entire active layer remain largely unavailable for the Qinghai–Tibet Plateau (QTP), which hosts the largest low- to mid-latitude permafrost region globally. Here, we present the first 90 m resolution, spatially complete dataset of ALM – the depth-averaged volumetric water content of the entire active layer – for the entire permafrost region of the QTP. The dataset was produced by integrating 342 in situ samples collected during peak-thaw seasons from 2009 to 2024 with multi-source remote sensing environmental predictors, topographic factors, and soil physicochemical properties. Four ensemble machine learning models, include Random Forest, Extra Trees, XGBoost, and CatBoost, were trained, and a bias-aware multi-model fusion strategy was applied to generate the final ALM product. SHAP-based recursive feature elimination was used to identify the dominant environmental controls and optimize feature selection. Random five-fold cross-validation showed high apparent accuracy (R² =0.62–0.63, RMSE ≈ 0.08 m³ m⁻³), and a group-based spatial cross-validation provided a conservative transferability estimate (pooled R² = 0.30–0.38). Within the permafrost region, ALM ranges from 0.02 to 0.70 m³ m⁻³, with a mean of 0.21 m³ m⁻³ and a standard deviation of 0.09 m³ m⁻³, exhibiting a distinct southeast-to-northwest decreasing gradient. The dataset is freely available with DOI: https://doi.org/10.12072/ncdc.permafrost.db7312.2026. The dataset is accompanied by an area-of-applicability (AOA) mask delineating the region – approximately 60 % of the permafrost region – where the cross-validated performance can be expected to hold, together with per-pixel uncertainty estimates derived from the DI–RMSE relationship and the divergence among the four models.