<p>Groundwater in arid and coastal regions is increasingly threatened by overextraction, seawater intrusion (SWI) and limited natural recharge, challenges that are common in data-scarce environments such as Qatar. This review evaluates how artificial intelligence (AI), particularly machine learning (ML), deep learning (DL) and semi-supervised learning (SSL), is advancing groundwater and SWI prediction under conditions of sparse, noisy and uneven monitoring data. Recent studies show that DL architectures, hybrid convolutional neural network–long short-term memory models and graph-based SSL approaches offer strong potential for capturing nonlinear, spatiotemporal dependencies in groundwater systems, outperforming many traditional modelling techniques. Key methodological challenges remain, including overfitting in DL models trained on limited datasets, weak model interpretability, and difficulty validating predictions where in-situ observations are scarce. To address these limitations, the review highlights emerging solutions such as physics-informed ML frameworks, explainable AI, transfer learning, synthetic data generation and multi-source data fusion. The role of remote sensing and sensor-network integration is also discussed as a means to expand data availability for groundwater quality and SWI monitoring. This review concludes with a roadmap for developing robust, interpretable and data-efficient AI systems designed for arid-region aquifers, providing guidance for future research and supporting the development of adaptive, sustainable groundwater management strategies.</p>