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 labor-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. This research paper 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.