Sustainable and renewable energy is the wave of the future, and scientists across the world are working harder than ever to perfect biomass conversion technology. Biomass has great promise as a carbon-neutral resource that can help meet the worlda s growing energy needs while reducing emissions of harmful gases. Nevertheless, energy losses are common in current conversion processes because of less-than-ideal operating circumstances and a lack of optimization in thermochemical and biochemical interactions. Improving the total efficiency of biomass conversion for high-yield renewable energy production is the main emphasis of this project, which aims to establish an integrated computational framework. Through the use of adaptive management of feedstock composition, reaction temperature, and pressure, the suggested conceptual algorithm integrates pyrolysis, gasification, and anaerobic digestion into a single optimization model, therefore minimizing conversion inefficiencies. Using a hybrid modeling method that combines process kinetics with data-driven optimization approaches, this study predicts the highest feasible power conversion efficiency (PCE) for different biomass feedstocks. This is what makes it unique. Results show that energy recovery is 6-8% better and carbon losses are reduced by up to 10% compared to traditional modeling tools like Aspen Plus and BioWin. The findings show that the suggested model might be a long-term, efficient, and scalable answer for current biomass power plants.