Bioenergy is one of the important sources of renewable energy that has gained increasing attention due to the limitations of fossil fuel resources, as well as environmental pollution and greenhouse gas emissions resulting from the combustion of fossil fuels. Biomass, as the raw material for bioenergy production, is obtained from various sources such as plant residues, forestry, and agricultural products. By estimating the amount of biomass available in a given area, a suitable assessment of the potential for bioenergy production can be made. Satellite imagery is one of the best tools for estimating biomass quantities. In this study, various methods of analyzing satellite data, including remote sensing, Geographic Information Systems (GIS), Google Earth Engine (GEE), machine learning, artificial intelligence, and growth modeling, have been evaluated for biomass estimation. The results show that models based on vegetation indices have an accuracy of between 71 and 88%, while the combination of multi-satellite data increases the prediction accuracy to 96%, outperforming traditional methods by about 10%. In the field of machine learning, artificial neural networks (ANN) and deep neural networks (DNN) showed mapping accuracy of up to 90% and 97%, respectively. The random forest (RF) and support vector machine (SVM) algorithms provided an accuracy of between 70–90%. These findings indicate that the combination of satellite data with advanced technologies has the potential to provide more accurate and stable estimates of biomass.