Dr. Abdul Mateen ahmed, Mohammed Fardan Ahmed, Mohammed Rizwan

The escalating global waste crisis necessitates innovative solutions for sustainable waste management and energy production. This study explores the integration of Artificial Intelligence (AI) in converting municipal solid waste into energy, aiming to optimize waste-to-energy (WTE) processes. Utilizing machine learning algorithms, we developed predictive models to assess waste composition and energy yield, enhancing the efficiency of WTE systems. The results demonstrate that AI-driven approaches can significantly improve energy recovery rates and operational efficiency in WTE facilities. This interdisciplinary approach offers a promising pathway toward sustainable waste management and energy production.