Machine Learning and Hybrid Approaches in the Energy Valorization of Contaminated Sludge: Global Trends and Perspectives

While the technological foundation for sludge valorization (anaerobic digestion and pyrolysis) is mature, a significant disconnect exists between traditional research and the advanced application of artificial intelligence. This study identifies that Machine Learning (ML) remains in a peripheral position, representing an untapped frontier for achieving predictive and circular systems. The methodology involved a quantitative bibliometric analysis of 190 Scopus-indexed documents (2005–2025). We analyzed indicators of scientific production, collaboration, and thematic evolution using Bibliometrix and VOSviewer 1.6.20. The results reveal a rapidly growing research field, predominantly led by Chinese institutions. The temporal analysis projects a productivity peak around 2033. Core topics include established technologies like anaerobic digestion and pyrolysis. However, network and keyword analyses reveal an emerging trend toward hydrothermal processes and, crucially, the early incorporation of ML. However, ML still occupies a peripheral position within the main scientific discourse, highlighting a gap between traditional research and the advanced application of artificial intelligence. The study systematizes existing knowledge and demonstrates that, although the technological foundation is mature, the deep integration of ML represents the future frontier for achieving sludge valorization systems that are more predictive, efficient, and aligned with the principles of the circular economy.