AI-Driven Solutions For Achieving SDG's: Harnessing Machine Learning To Address Global Sustainability Challenges

Abstract

The Sustainable Development Goals (SDGs) established by the United Nations offer a global guide to building peace, prosperity, and sustainability of the environment. Nevertheless, surveillance and reaching these objectives are a complex process with limited data, material availability, and overall inefficiency of the system. Artificial Intelligence (AI), notably Machine Learning (ML), can be presented as such a potent instrument that can help close such gaps, as advanced analytics, prediction models, and data-driven decision-making. This essay examines the use of AI-based solutions of which ML applications are the most widespread ones, how they are being utilized in different spheres to speed up the process of SDGs achievement. It researches the existing approaches, the studies connected with them, the practical applications, and the outcomes already recorded. Limitations, ethical implication, and future directions within the context of embedding AI into sustainability models are also the assessments within the study. The results indicate that although ML has a huge potential in improving sustainability monitoring, it needs to be implemented strategically and be governed in a way to make its operations inclusive and equitable to all.