This study presents an integrated assessment of above-ground biomass (AGB) and below-ground biomass (BGB) carbon stocks of dominant gymnosperms in Ayubia National Park, Pakistan. Sixty-three circular plots (0.1 ha each; 17.84 m radius) were established to estimate the carbon sequestration potential of key conifer species, quantify carbon stocks, validate AGB estimates using Sentinel-2 satellite imagery, and examine the correlation between spectral vegetation indices and biomass. A suite of regression models simple, multiple, and stepwise was employed to identify optimal predictors. Biomass estimates were further evaluated for their applicability to REDD (Reducing Emissions from Deforestation and Forest Degradation) + carbon accounting protocols. The maximum diameter at breast height (DBH) and height recorded for Pinus wallichiana (Wall. ex D. Don) A.B. Jacks., Abies pindrow (Royle ex D. Don) Royle, and Picea smithiana (Wall.) Boiss. were 74.00 cm and 33.95 m; 72.13 cm, and 34.65 m; and 70.45 cm and 32.00 m, respectively. AGB and BGB varied significantly among species: P. wallichiana (196.13–6.17 t/ha, 92.18–1.27 t/ha), A. pindrow (175.46–10.92 t/ha, 45.62–8.67 t/ha), and P. smithiana (174.63–5.03 t/ha, 45.40–3.99 t/ha). Mean AGB and above-ground carbon (AGC) ranged from 17.56 to 312.39 t/ha and 8.25 to 146.82 t/ha, respectively. Among spectral indices, NDVI (Normalized Difference Vegetation Index) demonstrated the strongest individual correlation with AGB (R² = 0.622, RMSE = 39.7 t/ha). However, a stepwise multi-index regression model significantly improved prediction accuracy (R² = 0.915, RMSE = 20.2 t/ha), reducing estimation error nearly fivefold. In contrast, the multi-band model performed poorly (R² = 0.37, RMSE = 80 t/ha), likely due to overfitting. These results confirm that NDVI is a strong standalone predictor of biomass, while the stepwise index model offers the most reliable estimation method for carbon stock assessment.