Abstract
This study introduces a framework aimed at enhancing the role of Artificial Intelligence (AI) in achieving the Sustainable Development Goals (SDGs). The primary objective is to address key challenges in AI applications, such as data scarcity, ethical concerns, and cultural diversity, by integrating explainable AI (XAI), simulation environments, and modular customization. The study emphasizes on region-specific datasets, synthetic data generation, and iterative refinement to improve AI solutions in sectors like poverty, healthcare, and climate action. The findings emphasizes on AI’s potential to transform theoretical solutions into practical, scalable implementations, driving sustainable development. While addressing challenges like data quality, algorithmic bias, and regulatory issues, the study also highlights the importance of ethical principles and contextual adaptability to achieve long-term, inclusive progress toward the SDGs.