Accurate spatial measurement of aboveground biomass (AGB) is essential for assessing carbon stocks in the forest ecosystem. To enhance this estimation, integrating active and passive Earth Observation data with advanced machine learning techniques offers a promising approach. This study presents an integrated HybridEnsemble model with golden jackal optimization for AGB estimation and evaluates its predictive performance against individual base-learners, including categorical boosting (CatBoost), extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), and adaptive boosting (AdaBoost) via the synergistic application of Explainable Artificial Intelligence (XAI) and active and passive datasets. The findings revealed a clear performance ranking among these models, with the HybridEnsemble Golden Jackal Optimization (HGJO) model identified as the most effective, which yielded a correlation coefficient (R2) of 0.821 and a Relative Root Mean Square Error (rRMSE) of 16.30%. This performance was followed by CatBoost (R2 = 0.816, rRMSE = 16.51%), LightGBM (R2 = 0.804, rRMSE = 17.05%), XGBoost (R2 = 0.802, rRMSE = 17.13%), and AdaBoost (R2 = 0.731, rRMSE = 19.97%), with all comparisons reported at 95% confidence intervals. XAI revealed that predictors from optical sensors (passive) were strongly correlated with AGB and played a significant role in predicting AGB, while features derived from SAR (synthetic aperture radar, an active sensor), less influential, provided unique backscatter and context-specific insights that enhanced the model’s performance. Forecast results indicate an increasing trend. Additionally, the analysis revealed that future AGB accumulation in the subtropical forest of Hong Kong will be highly variable and strongly dependent on initial biomass levels, with high-biomass plots likely to see the greatest gains. However, spatial uncertainty in AGB predictions varied across the study area, with higher uncertainties observed in forested areas and lower uncertainties in urban areas. Overall, this study not only enhances understanding of optimized hybrid ensemble models for biomass prediction but also offers valuable insights for forecasting forest dynamics, supporting sustainable forest management and carbon stock monitoring globally.