Groundwater pollution and management pose significant challenges in maintaining sustainable water resources, especially as global demand for clean water rises. Integrating Artificial Intelligence (AI) and Machine Learning (ML) into groundwater management offers innovative solutions to these challenges. This chapter explores the novel application of AI and ML techniques to control groundwater pollution and enhance management practices. AI and ML algorithms can analyze vast datasets from various sources, including remote sensing, geographic information systems (GIS), and historical records, to accurately predict contamination events and identify pollution sources. By leveraging predictive analytics, these technologies can forecast groundwater quality trends, enabling proactive measures to mitigate potential pollutants. Machine learning models, such as neural networks and support vector machines, are employed to develop robust groundwater contamination prediction systems. These models can learn complex patterns and relationships within the data, facilitating early detection of pollution incidents and optimizing remediation strategies. Additionally, AI-driven optimization techniques are used to design efficient monitoring networks and manage groundwater extraction sustainably. This ensures that water withdrawal does not exceed replenishment rates, preserving aquifer health. Advancements in future work will be driven by the further implementation of deep learning, explainable artificial intelligence (XAI), and other cutting-edge techniques. As discussed, these advancements portray AI and ML's application to sparsely study variables, model new or unique study areas, and implement ML techniques for comprehensive groundwater quality management. This chapter delves into case studies where AI and ML have been successfully implemented in groundwater management, highlighting their positive impact on environmental conservation and resource sustainability. Challenges such as data quality, model interpretability, and the need for interdisciplinary collaboration are discussed to provide a comprehensive understanding of the current state and future potential of AI and ML in this field.