Groundwater is a vital component of global freshwater resources, sustaining ecosystems, agriculture, and human livelihoods. However, increasing pressures from agricultural intensification, industrialization, population growth, and climate change have significantly affected both its quantity and quality. Addressing these complex and nonlinear challenges requires advanced analytical tools beyond traditional hydrological modeling. Artificial intelligence (AI) has emerged as a transformative approach for improving groundwater research and management through its ability to process large datasets, uncover hidden patterns, and enhance predictive accuracy. This chapter reviews the evolution and current applications of AI in groundwater science, emphasizing methods such as artificial neural networks, support vector machines, and hybrid models for predictive modeling, real-time monitoring, data integration, decision support, and optimization. Key advantages of AI over conventional approaches include its adaptability, efficiency, and capability to model complex subsurface processes. The chapter also highlights prevailing challenges, such as data quality, interpretability, and technical expertise, and outlines emerging research directions in AI-enabled risk assessment and collaborative platforms. These developments demonstrate that AI-based approaches are reshaping groundwater science, offering pathways toward sustainable, data-driven, and resilient groundwater management under changing environmental conditions.