Artificial intelligence applications in achieving the sustainable development goals: A systematic review using context-based data crawling techniques

This systematic review explores the pivotal role of artificial intelligence (AI) in advancing the United Nations' Sustainable Development Goals (SDGs), with a focus on context-based data crawling techniques such as web scraping, social media data mining, and IoT sensor data collection. AI's ability to process large, dynamic datasets in real-time has significantly contributed to sectors such as healthcare (SDG 3), education (SDG 4), energy management (SDG 7), and climate action (SDG 13). In healthcare, AI models enhanced public health monitoring and disease diagnosis, while in education, AI-driven personalized learning platforms improved adaptive learning outcomes. In energy management, AI optimized smart grids and renewable energy systems, and in climate action, it contributed to more accurate environmental monitoring and disaster prediction. Despite these advancements, challenges such as data privacy, algorithmic bias, cybersecurity concerns, and limited access to high-quality datasets in low-resource regions remain. Addressing these issues through transparent governance and ethical AI development is critical to maximizing AI's impact on global sustainability efforts.