Groundwater systems rank among the world’s most vital components of the Earth. In the world today, there exists a growing freshwater need for half of the world’s population for domestic water supply to satisfy domestic needs, feed agriculture, industries, and to sustain the natural environment. However, rising concerns such as over-extraction, pollution, and climate change have exposed the limits of the classical physical-based models of groundwater, which need long data sets, computational experience, and simplifications. In response, Artificial Intelligence (AI) is providing a major change in approaches to groundwater modelling, management, and forecasting. This chapter consolidates the state-of-AI in groundwater science, surveying machine learning (ML), deep learning (DL), and hybrid approaches that combine physical theory with data-driven intelligence. Random Forests, Support Vector Machines, and Artificial Neural Networks have shown brilliant capability in forecasting groundwater levels, recharge, and risk of contamination, especially under data-scarce conditions. Deep learning models, particularly the Recurrent Neural Network, further augment temporal and spatial forecasting capability. The chapter also reviews the promise of AI-based decision-support systems under sustainable groundwater governance, particularly in the Global South, where monitoring networks and data infrastructures remain underdeveloped. AI incorporation into groundwater hydrology presents an unprecedented leverage point that can raise accuracy, enhance management, and foster adaptive resilience amidst growing worldwide water insecurity. There is a need for interdisciplinary partnerships, open-access data sets, and ethical guidelines towards equitable and sustainable AI applications worldwide in groundwater management.