Research trends in the application of machine learning in sustainability practices based on a bibliometric analysis - Alejandro Valencia-Arias; Manuel Humberto Vásquez Coronado; Milagros Layesvka Clavo Medina; Maricarmen Jeneth Morales Garcia; Ana Rosa Requejo Arias; Josias Idelso Saavedra Tirado (Sustainable Futures, 2025)
The advent of technological advancements and mounting environmental concerns have given rise to an interest in the role of machine learning in promoting sustainability. However, research findings underscore the presence of under-explored areas that necessitate further investigation. The present study employs the PRISMA-2020 methodology to analyze trends from Scopus and Web of Science, revealing a growing interest, particularly from countries such as China, Saudi Arabia, and Spain. Notable contributors to this field include authors such as Nilashi and Ahmadi, along with journals such as the Journal of Cleaner Production and Sustainability. The prevailing focus in this field has shifted towards the treatment of waste, the application of deep learning techniques, and the pursuit of sustainable development goals. This shift underscores the growing significance of concepts such as artificial intelligence and the emergence of keywords like XGBoost. This research makes a unique contribution by revealing underexplored thematic evolutions and the emergence of cutting-edge concepts such as XGBoost and blockchain integration in sustainability research. The bibliometric approach applied offers a novel mapping of interdisciplinary intersections, highlighting the transformation of machine learning applications from experimental to policy-relevant domains.