<![CDATA[Aboveground biomass plays a pivotal role in estimating tropical forest carbon stocks, particularly in Southeast Asia, a region rich in biodiversity but threatened by deforestation and land-use change. This systematic review analyzes 71 peer-reviewed studies published between 1995 and 2025, selected from an initial pool of 8,509 articles. The review aims to evaluate methodological developments and performance across three major approaches: field-based and allometric models, remote sensing including Unmanned Aerial Vehicle (UAV) platforms, and Machine Learning (ML) with data fusion, within key tropical forest countries: Indonesia, Malaysia, and Vietnam. These countries were selected due to their high forest cover, rapid land-use change, and central roles in the implementation of Reducing Emissions from Deforestation and Forest Degradation (REDD+). Field-based models, particularly those calibrated locally, consistently produced high accuracy, with R² values generally ranging from 0.80 to 0.96. Remote sensing techniques, particularly the integration of airborne LiDAR and optical–SAR, demonstrated strong predictive performance (R² > 0.85) and relatively low Root Mean Square Error (RMSE), typically below 30 Mg/ha. ML approaches such as Random Forest, Support Vector Machines, and LightGBM also achieved competitive results, with R² typically between 0.75 and 0.85 and RMSE below 40 Mg/ha when trained on high-quality input data. Mangrove and dipterocarp forests emerged as the most frequently studied ecosystems. While methodological innovations are evident, notable gaps remain in model harmonization and representation of ecosystem diversity. The review recommends integrating species-specific allometric models with remote sensing and machine learning pipelines, supported by open-access datasets, to enhance national forest monitoring systems and REDD+ readiness across Southeast Asia. Keywords: aboveground biomass, allometric, biomass estimation, carbon stock, South East Asia]]>
