Groundwater science in the age of AI: emerging paradigms and challenges

Groundwater is a vital resource for drinking water, agriculture, and industry, yet its sustainability is increasingly threatened by over-extraction, contamination, and climate variability. This review synthesizes recent advances in artificial intelligence (AI) for sustainable groundwater management, focusing on four key domains: predictive modeling, quality assessment, resource optimization, and integration with remote sensing and internet of things (IoT). We highlight how emerging AI methods spanning machine learning, deep learning, and hybrid frameworks enhance forecasting accuracy, contaminant detection, and real-time decision support. Unlike previous reviews that broadly address AI in hydrology, this work uniquely consolidates groundwater-specific applications, identifies critical research gaps, and introduces emerging paradigms such as explainable AI and digital twin frameworks. We conclude by outlining a research agenda for data-driven, adaptive, and transparent groundwater governance under accelerating global water stress.