The proliferation of trace and emerging contaminants (TECs) in natural water resources poses significant challenges to developing world, where limited infrastructure and resources constrain traditional monitoring methods. While artificial intelligence (AI) has shown promise in various environmental applications, its potential for modeling TECs in developing countries remains largely unexplored. This chapter addresses this knowledge gap by providing a comprehensive, reflective analysis of how machine learning and deep learning technologies can transform water quality monitoring and prediction in resource-limited regions. Through systematic reflections and analysis of implementation cases across multiple developing countries, the chapter examines the transition from conventional to AI-based approaches in both surface water and groundwater quality assessment. The investigation reveals that most AI-based systems applied consistently achieve acceptable, prediction accuracies for various TECs. Key findings demonstrate that the integration of machine learning with emerging technologies such as Internet of Things (IoT) sensors and remote sensing can overcome traditional monitoring limitations, while cloud computing platforms could make sophisticated modeling techniques accessible to developing nations despite infrastructure constraints. The study also identified critical success factors for AI implementation in water research, including standardized data collection procedures, capacity building initiatives, and supportive policy frameworks. These findings have important implications for water resources management in developing countries, suggesting that strategic investment in AI-based monitoring systems could provide cost-effective solutions for addressing the challenges of TECs. Conclusively, while challenges remain in areas such as data availability and technical expertise, the potential benefits of AI implementation in developing countries far outweigh the obstacles, particularly when supported by appropriate policy frameworks and international cooperation.