Biomass valorization and agricultural waste management are cornerstones of environmentally responsible farming and power generation. Valorization of agricultural leftovers offers the opportunity to generate renewable energy and value-added goods, but their buildup causes environmental difficulties. This research presents a novel strategy for improving the efficacy and efficiency of biomass valorization and agricultural waste management by combining the Random Forest (RF) algorithm with Internet of Things (IoT) technology. A complete dataset on patterns, kinds, and amounts of waste may be obtained via the use of IoT architecture, which allows for real-time monitoring and data collecting from agricultural fields. Using this information as a starting point, the RF algorithm can forecast which agricultural wastes have the most promising as biomass energy and bioproduct candidates. To determine which waste-to-biomass conversion paths are the best in terms of energy production, economic feasibility, and environmental effect, it may use the RF algorithm, which is famously good at classification and regression works. By combining the IoT with RF algorithms, a comprehensive solution is provided optimizes the biomass valorization chain and improves decision-making in agricultural waste management. Experimental results from a variety of agricultural contexts show the system may improve biomass energy production efficiency, reduce waste disposal costs, and contribute to the circular economy. Its findings show advanced digital technology and machine learning algorithms may solve the problems with agricultural waste management and bring biomass's full sustainable resource potential to light. © 2025 Elsevier B.V., All rights reserved.