In recent years, estimating forest parameters such as Tree Height (CH) and AboveGround Biomass (AGB) has gained importance due to their essential role in understanding the global carbon cycle, mitigating climate change, and preventing biodiversity loss. Accurate inference of these parameters is crucial because they are key indicators of forest health and carbon storage capacity. The Brazilian Amazon, a vital tropical forest, plays a crucial role in absorbing as much carbon as is released through deforestation and degradation. Understanding and monitoring CH and AGB enable better management and conservation strategies and promote sustainable practices. Traditionally, these forest parameters have been estimated through ground-based methods, such as forest inventory plots, which involve physically measuring trees. While these methods are highly accurate, they are labor-intensive and often impractical for large-scale assessments due to the vast and inaccessible nature of forests. Additionally, the application of Machine Learning (ML) and Deep Learning (DL) techniques offers significant advantages over traditional methods, providing rapid and scalable solutions for estimating forest parameters across extensive areas. Moreover, they can integrate data from various sources, enhancing the robustness of the estimates. While many studies have utilized forest inventory plots, RS, and ML techniques, DL techniques remain underexplored in studies within the Brazilian Amazon. This study aims to evaluate DL techniques for estimating TH and AGB in dense tropical forests using various RS imagery, including Sentinel-1, ALOS-2/PALSAR-2, Sentinel-2, and GEDI. Three DL models were tested for CH estimation, where the best of the models achieve a R(2) of 0.751, an MAE of 4.068 meters, and an RMSE of 5.737 meters. Furthermore, various ML techniques were evaluated for AGB estimation, resulting in an R(2) of 0.648, an MAE of 48.842 Mg·ha(-1), and RMSE of 70.745 Mg·ha(-1).