This article explores the integration of artificial intelligence (AI) into waste-to-energy (WtE) technology to enhance efficiency, sustainability, and environmental impact. The study highlights how AI can optimize waste sorting, process monitoring, and energy production forecasting, leading to more efficient conversion of waste into energy and reduced environmental harm. AI's ability to analyze large datasets, predict maintenance needs, and improve operational accuracy makes it a valuable tool in modern WtE systems. The research discusses the practical applications of AI in WtE workflows, emphasizing its potential to revolutionize the sector by improving technical performance and aligning with environmental and economic sustainability goals. The study also addresses the challenges of implementing AI in WtE technologies, including the need for high-quality data and the complexities of integrating AI with existing systems. Through case studies and analysis, the research demonstrates that AI can significantly reduce environmental footprints, promote a circular economy, and improve the sustainability of waste management and energy production. The findings show that AI integration can increase energy conversion efficiency by up to 50%, reduce harmful emissions by 24–34%, and cut operational costs by 25%, while also improving system reliability. The article concludes that AI has the potential to transform WtE technology by making it more efficient, sustainable, and economically viable. However, future research should focus on advancing AI techniques such as deep learning and reinforcement learning, as well as addressing the socio-economic impacts of AI integration in the sector. The study provides valuable insights into how AI-driven innovations can lead to more sustainable and efficient waste management and energy production systems, paving the way for future advancements in this critical field.This article explores the integration of artificial intelligence (AI) into waste-to-energy (WtE) technology to enhance efficiency, sustainability, and environmental impact. The study highlights how AI can optimize waste sorting, process monitoring, and energy production forecasting, leading to more efficient conversion of waste into energy and reduced environmental harm. AI's ability to analyze large datasets, predict maintenance needs, and improve operational accuracy makes it a valuable tool in modern WtE systems. The research discusses the practical applications of AI in WtE workflows, emphasizing its potential to revolutionize the sector by improving technical performance and aligning with environmental and economic sustainability goals. The study also addresses the challenges of implementing AI in WtE technologies, including the need for high-quality data and the complexities of integrating AI with existing systems. Through case studies and analysis, the research demonstrates that AI can significantly reduce environmental footprints, promote a circular economy, and improve the sustainability of waste management and energy production. The findings show that AI integration can increase energy conversion efficiency by up to 50%, reduce harmful emissions by 24–34%, and cut operational costs by 25%, while also improving system reliability. The article concludes that AI has the potential to transform WtE technology by making it more efficient, sustainable, and economically viable. However, future research should focus on advancing AI techniques such as deep learning and reinforcement learning, as well as addressing the socio-economic impacts of AI integration in the sector. The study provides valuable insights into how AI-driven innovations can lead to more sustainable and efficient waste management and energy production systems, paving the way for future advancements in this critical field.