Forecasting Artificial General Intelligence for Sustainable Development Goals: A Data-Driven Analysis of Research Trends

Artificial general intelligence (AGI) is often depicted as a transformative breakthrough, yet debates persist on whether current advancements truly represent general intelligence or remain limited to domain-specific applications. This study empirically maps AGI-related research across subject areas, geographies, and United Nations Sustainable Development Goals (SDGs) via machine learning-based analysis. The findings reveal that while the AGI discourse remains anchored in computing and engineering, it has diversified significantly into human-centered domains such as healthcare (SDG 3), education (SDG 4), clean energy (SDG 7), industrial innovation (SDG 9), and public governance (SDG 16). Geographically, research remains concentrated in the United States, China, and Europe, but emerging contributions from countries such as India, Pakistan, and Costa Rica suggest a gradual democratization of AGI exploration. Thematic expansion into legal systems, governance, and environmental sustainability points to AGI’s growing relevance for systemic societal challenges, even if true AGI remains aspirational. Funding patterns show strong private and public sector interest in general-purpose AI systems, whereas institutional collaborations are increasingly global and interdisciplinary. However, challenges persist in cross-sectoral data interoperability, infrastructure readiness, equitable funding distribution, and regulatory oversight. Addressing these issues requires anticipatory governance, international cooperation, and capacity-building strategies to ensure that the evolving AGI landscape aligns with inclusive, sustainable, and socially responsible futures.