Groundwater flow modelling is critical for managing groundwater resources, particularly amid climate change and rising water demand. This narrative review examines the role of groundwater flow models in sustainable water resource management, focusing on challenges and solutions to enhance model reliability. A key challenge is data limitation—especially in regions like sub-Saharan Africa and South Asia, where scarce hydrogeological data hinders accurate model calibration. The complexity of aquifer systems, such as karst aquifers in North America and fractured-rock aquifers in India, further complicates model development, requiring detailed geological data and complex simulations. Additionally, uncertainties arise from limited knowledge of aquifer properties, variable boundary conditions, and sparse monitoring networks, which can reduce model predictability. Despite these obstacles, groundwater flow models are essential for simulating groundwater behaviour in response to altered precipitation patterns, increasing extraction rates, and extreme events like droughts. For instance, predictive modelling has helped assess potential depletion risks in California’s Central Valley and contamination risks in industrial zones of East Asia, guiding sustainable extraction strategies and contamination assessments. To improve model reliability, this review emphasizes the need for enhanced data collection, integration of advanced technologies—such as artificial intelligence and machine learning for predictive accuracy—and the adoption of multidisciplinary modelling approaches. These advancements, improved sensor networks, and regional data-sharing initiatives are critical to reducing uncertainties and increasing model precision. Ultimately, such improvements will support climate adaptation efforts and promote the sustainable management of global groundwater resources, benefiting water managers and policy makers.
