In the field of boiler operations, biomass boilers have specific attention due to their high consumption of firewood to generate energy, which leads to natural resource degradation. This research is implemented with three main objectives: to design a custom multi-output neural network model that accurately predicts key energy parameters, including firewood amount; to develop a user-friendly web application that delivers these predictions in real time; and to ultimately predict and monitor energy wastage to reduce the wastage of natural resources. To achieve these objectives, a dedicated data collection system was implemented to gather essential parameters such as the energy produced by firewood in real time. By using the collected data, a custom multi-output neural network was developed using Python and Jupyter Labs to predict the intended outcomes, such as firewood amount. Another web application was also developed to provide the users with a user-friendly output. The predictive capabilities are shown as follows: The neural network achieved R2 0.9728 for overall firewood amount, depicting the precision, robustness, and near-ideal predictive capabilities of the machine learning model. In summary, the research reveals the exceptional prediction ability of the custom multi-output neural network that provides accurate and reliable energy prediction and reveals a smart pathway to intelligent, efficient, and sustainable boiler operations. By merging real-time monitoring with advanced data modeling, the research demonstrates a significant step toward optimizing energy use and managing natural resources in biomass boiler systems.