Machine Learning and the Sustainable Development Goals: Theoretical Insights and Practical Applications

Machine Learning (ML) and Artificial Intelligence (AI) have become powerful tools for overcoming complex global challenges in harmony with the Sustainable Development Goals (SDGs) of the United Nations. In this article, we illustrate ML and AI technology's contribution to sustainable development through theoretical and practical examples in a variety of sectors. In this article, AI-powered interventions in healthcare, agriculture, greenhouse gas emission reduction, environment tracking, and education have been analyzed. Generative AI technology has changed access to education and personalized learning, and environmental tracking and conservation have been aided through machine learning algorithms. Despite such positive development, considerable obstacles include a lack of data, algorithm bias, ethics, and interpretability of complex AI algorithms. All such impediments remind us of multi-sectoral collaboration and responsible AI intervention for delivering equitable and sustainable development. According to the article, overcoming obstacles necessitates transparent and participatory frameworks and deliberate collaborations between governments, private industries, academe, and civil society groups. With full realization of ML and AI through ethics and participatory policies, we can mobilize effective, evidence-guided interventions and hasten success towards attaining the SDGs. With a demand for ongoing studies in case files for responsible AI interventions with a strong bias for equity, consideration, and humanity, in this article, a clarion call for such studies is placed.