Artificial Intelligence and Digital Twins for Sustainable Waste Management: A Bibliometric and Thematic Review

Sustainable waste management is a critical challenge for ecological transitions. Emerging technologies such as Artificial Intelligence (AI) and Digital Twins (DT) offer new opportunities to optimize collection, treatment, and valorization processes, thereby promoting circular economy models. This study adopts an integrated approach to analyze the state of the art and key research trajectories related to the application of these technologies in waste management. Through a bibliometric analysis based on the Scopus database and mapping with VOSviewer (version 1.6.20), three main thematic clusters were identified: (i) predictive and environmental methods, (ii) sustainability and optimization, and (iii) monitoring and environmental impacts. A qualitative analysis of the 20 most-cited articles further revealed six major research areas, including waste forecasting, recycled materials, process digitalization, and intelligent environmental monitoring. The findings indicate a growing convergence among digitalization, automation, and sustainability. The adopted approach enables the mapping of major research directions and emerging interconnections among AI, the circular economy, and predictive management, providing an up-to-date and systemic perspective on the field.