Artificial Intelligence in Environmental Engineering: Mapping Research Progress through Bibliometrics

Abstract

The integration of artificial intelligence (AI) into environmental engineering has emerged as a transformative approach to addressing global sustainability challenges. This study conducts a comprehensive bibliometric analysis to map research trends, identify key contributors, and explore thematic clusters in this interdisciplinary domain. Using a dataset extracted from Scopus (2000–2025) and tools like VOSviewer, the study examines publication trends, geographical contributions, co-authorship networks, and keyword co-occurrences. The results reveal a rapid increase in research activity, with significant contributions from leading countries like China, the United States, and India. Thematic clusters highlight the dominance of machine learning, sustainability, and climate change, alongside emerging trends like IoT, blockchain, and optimization. The study identifies research gaps, including limited representation from low-income regions and underexplored areas like explainable AI and socio-economic integration. This analysis provides critical insights into the current research landscape and outlines future directions for advancing AI-driven solutions in environmental engineering.