Forests constitute the central part of terrestrial ecosystems, encompassing approximately 90% of the total biomass. They are responsible for over 65% of the annual carbon sequestration, playing a crucial role in maintaining global carbon balance and mitigating the increase of greenhouse gases. Tianshan Spruce, a vital forest resource in the Xinjiang region, covers an area of 758,600 hectares, accounting for 42.33% of the total area, with a volume of 0.17 cubic kilometers, representing 50.66% of the total volume. Constructing a spatiotemporal biomass dataset of Tianshan Spruce provides a scientific basis for assessing the carbon sequestration potential and for the protection and sustainable management of these forests in the region. In recent years, biomass estimation models integrating multisource data like remote sensing, meteorological, topographic, and soil data have been widely adopted. The geographically weighted regression (GWR) method is particularly effective in addressing spatial heterogeneity issues in forestry and ecological applications. This study's dataset is grounded on field survey data from dense Tianshan Spruce areas collected in 2002, 2007, 2012, and 2017 by the project team, adhering to the forest survey technical procedures of the National Forestry and Grassland Administration, and on multisource remote sensing images from the Google Earth Engine. The geographically and temporally weighted regression (GTWR) model was employed to generate biomass distribution maps for each period. This dataset is instrumental in exploring the growth trends and biomass variations of Tianshan Spruce, holding scientific value for ecological protection and climate change research in the Tianshan area of Xinjiang. It has practical significance for regional ecosystem management. Additionally, it offers data support for identifying and protecting key ecological areas, assessing the impact of climate change on ecosystems, monitoring and simulating the carbon cycle processes in ecosystems, and contributing to global carbon budget research. Furthermore, it provides valuable data resources for researchers in ecosystem management and related fields.