Artificial Intelligence and Machine Learning for SDG-Oriented Decision Making in Public Governance: A Bibliometric Analysis and Systematic Literature Review

This study maps the development of literature on artificial intelligence (AI) and machine learning (ML) in public decision-making oriented toward achieving the Sustainable Development Goals (SDGs). A bibliometric analysis was conducted on 380 Scopus-indexed documents published between 2017 and 2026, while a focused systematic literature review was performed on 17 studies selected through PRISMA-based screening. Data were cleaned using OpenRefine and analyzed with VOSviewer and Bibliometrix to identify keyword networks, thematic trends, author productivity, country collaboration, and scientific development structures. The findings show that AI and ML are central themes in SDG-oriented decision-making research, strongly connected to decision support systems, public policy, sustainability, climate change, environmental sustainability, smart cities, energy, health, and public governance. Publication growth accelerated markedly after 2023, with India and China as dominant contributors, although international collaboration remains limited. The review also reveals major challenges related to explainability, including black-box algorithms, data bias, low transparency, weak data governance, limited institutional capacity, and unclear legal and ethical accountability. This study argues that AI/ML for SDGs must move beyond accuracy and efficiency toward explainable, auditable, trustworthy, and socially accountable decision support systems globally.