From Data to Sustainability: A Systematic Bibliometric Review of Artificial Intelligence and Machine Learning Applications

This study provides a comprehensive systematic review and bibliometric mapping of artificial intelligence and machine learning applications within sustainability research. A bibliometric analysis was conducted on 2981 publications retrieved from the Scopus database, covering the period from 2003 to 2025 and tracing the field’s evolution from fragmented early studies to rapid growth after 2018. The findings reveal a robust methodological core centered on deep learning and neural networks, increasingly applied to energy efficiency, precision agriculture, and smart urban ecosystems. A critical contribution of this review is the identification of the emergence of Green AI, highlighting the dual challenge of using artificial intelligence for environmental goals while mitigating the carbon footprint of computational processes themselves. Ultimately, this study offers a strategic roadmap for researchers and policymakers to align algorithmic innovation with global sustainable development goals.