Accurate estimation of above-ground biomass (AGB) plays a crucial role in various ecological and environmental studies. Traditional AGB estimation methods often rely on field measurements and labour-intensive approaches, limiting their scalability and efficiency. In recent years, the emergence of deep learning algorithms has shown promising results in AGB estimation using remote sensing data. These algorithms analyse input data such as remote sensing images or LiDAR data to learn complex patterns and relationships, enabling accurate estimation of AGB without relying on traditional manual methods or field measurements. This research work aims to provide a comprehensive review and analysis of the application of deep learning algorithms for AGB estimation, highlighting their advantages, limitations, and future research directions.