Soil carbon sequestration is a vital strategy for mitigating climate change, particularly in ecologically sensitive regions like the Himalayas. This study evaluates the spatio-temporal dynamics of carbon stock potential in Sirmaur district of Himachal Pradesh over a period of 30 years (1993–2023). To achieve this, multi-temporal Landsat satellite imagery (30 m resolution) for the years 1993, 2003, 2013, and 2023 was used to generate Land Use Land Cover (LULC) maps through random forest classification in a GIS environment. The resulting LULC maps served as key inputs for the InVEST (Integrated Valuation of Ecosystem Services and Tradeoffs) Carbon Storage and Sequestration model. This model quantifies total carbon stock by aggregating above-ground biomass, below-ground biomass, soil organic carbon, and dead organic matter across various land use categories. The results showed a steady increase in higher carbon density zones over the study period. Notably, the area under the “Very High” carbon density class (>15.6852 Mg/ha) expanded from 1,276.86 sq.km in 1993 to 1,379.66 sq.km in 2023. Conversely, the “Low” carbon density class (0–7.826 Mg/ha) reduced from 836.04 to 679.24 sq.km. Forests and agroforestry systems emerged as the dominant contributors to total carbon stock. This research highlights the importance of remote sensing and modeling frameworks in understanding carbon dynamics. It also provides scientific evidence to support climate-resilient land management and policy planning for carbon sequestration, particularly in fragile mountainous ecosystems.