Artificial intelligence (AI) has emerged as a key tool in optimizing waste-to-energy (WtE) conversion technology, addressing environmental challenges while promoting sustainable energy sources. This study explores the role of AI in enhancing the efficiency and effectiveness of WtE processes through automated waste sorting, process monitoring, and energy production forecasting. AI integration streamlines operations, improves data accuracy, and enhances the conversion of waste into energy, reducing environmental impacts and supporting sustainable practices. The research highlights the practical applications of AI in optimizing the entire WtE workflow, emphasizing its potential to revolutionize the sector. It also addresses challenges and future prospects for AI implementation in WtE technologies, demonstrating that AI can significantly reduce environmental footprints and promote a circular economy. AI contributes to more sustainable and efficient waste management and energy production systems by improving technical performance and aligning with broader environmental and economic sustainability goals. The study shows that AI can enhance energy conversion efficiency, reduce emissions, and lower operational costs. For instance, energy conversion efficiency increased from 30% to 45%, and emissions of CO₂, NOₓ, and SO₂ were reduced by 24%, 27%, and 34%, respectively. Operational costs decreased by 25%, and maintenance downtime was reduced by 43%, improving system reliability. These results underscore the economic and environmental benefits of AI in WtE technology. The integration of AI into WtE technology has demonstrated significant advancements in operational efficiency, environmental sustainability, and economic viability. AI optimizes operational parameters, leading to a 50% increase in energy output. The study also highlights the potential for future research, including the application of advanced AI techniques such as deep learning and reinforcement learning, to further enhance performance. Additionally, the socio-economic impacts of AI integration in WtE technology, including the need for workforce retraining, are considered. The findings provide valuable insights into the transformative potential of AI in creating more efficient, sustainable, and resilient waste management and energy production systems.Artificial intelligence (AI) has emerged as a key tool in optimizing waste-to-energy (WtE) conversion technology, addressing environmental challenges while promoting sustainable energy sources. This study explores the role of AI in enhancing the efficiency and effectiveness of WtE processes through automated waste sorting, process monitoring, and energy production forecasting. AI integration streamlines operations, improves data accuracy, and enhances the conversion of waste into energy, reducing environmental impacts and supporting sustainable practices. The research highlights the practical applications of AI in optimizing the entire WtE workflow, emphasizing its potential to revolutionize the sector. It also addresses challenges and future prospects for AI implementation in WtE technologies, demonstrating that AI can significantly reduce environmental footprints and promote a circular economy. AI contributes to more sustainable and efficient waste management and energy production systems by improving technical performance and aligning with broader environmental and economic sustainability goals. The study shows that AI can enhance energy conversion efficiency, reduce emissions, and lower operational costs. For instance, energy conversion efficiency increased from 30% to 45%, and emissions of CO₂, NOₓ, and SO₂ were reduced by 24%, 27%, and 34%, respectively. Operational costs decreased by 25%, and maintenance downtime was reduced by 43%, improving system reliability. These results underscore the economic and environmental benefits of AI in WtE technology. The integration of AI into WtE technology has demonstrated significant advancements in operational efficiency, environmental sustainability, and economic viability. AI optimizes operational parameters, leading to a 50% increase in energy output. The study also highlights the potential for future research, including the application of advanced AI techniques such as deep learning and reinforcement learning, to further enhance performance. Additionally, the socio-economic impacts of AI integration in WtE technology, including the need for workforce retraining, are considered. The findings provide valuable insights into the transformative potential of AI in creating more efficient, sustainable, and resilient waste management and energy production systems.