The burgeoning demand for sustainable energy sources has catalyzed interest in Waste-to-Energy solutions offering both environmental benefits and energy generation potential. This paper explores the integration of machine learning (ML) techniques to optimize and predict key aspects of the Waste-to-Energy process. The study leverages historical data from waste facilities incorporating variables such as waste composition, operational parameters, and environmental conditions. In this paper, machine learning models, including regression, classification, and ensemble methods, are employed to optimize combustion efficiency, predict energy output, and enhance the overall operational performance of Waste-to-Energy conversion process. Furthermore, the predictive analytics are employed to anticipate maintenance needs for mitigating the downtime and optimizing the resource allocation. The findings contribute to the growing field of the sustainable energy by showcasing the efficacy of machine learning in Waste-to-Energy systems providing a scalable and adaptive solution for the challenges inherent in this dynamic and complex process.