Waste-to-Energy (WtE) technologies have gained momentum in the United States due to the urgent need for green waste management facilities. Predictive analytics is an innovative solution to optimize the efficiency and sustainability of WtE systems using effective waste collection, resource management, and energy production. This article outlines the application of predictive analytics in improving WtE technologies, including gasification, anaerobic digestion, and incineration plants. Using big data, machine learning, and real-time analytics, predictive models help stakeholders forecast trends in waste generation, maximize operating efficiency, and minimize environmental impact. Despite these advantages, data inaccuracy, regulatory issues, and complexity in integration are significant challenges to large-scale deployments. With the aid of a strict analysis of real-case studies, this review highlights proper applications of predictive analytics to improve WtE processes, waste segregation, and predictive maintenance. In addition, the article incorporates a graphical representation of data to depict significant trends in waste production, technological advancements, and the potential of AI-based models in circular economy practices. The findings emphasize the importance of public-private partnerships, investment in data infrastructure, and policy frameworks that facilitate the integration of predictive analytics into WtE systems. The review concludes with pragmatic recommendations to optimize sustainability, technological innovation, and waste management practices in the United States.