The sustainable management of global groundwater resources is increasingly challenged by climatic uncertainty and escalating anthropogenic stress. Thus, there is a need for simulation tools that are more robust and flexible. This systematic review addresses the integration of two dominant modeling paradigms: the physically grounded Modular Finite-Difference Flow (MODFLOW) model and the data-agile Artificial Neural Network (ANN). While the MODFLOW model provides deep process-based understanding, it is often limited by extensive data requirements and computational intensity. In contrast, an ANN offers remarkable predictive accuracy and computational efficiency, particularly in complex, non-linear systems, but traditionally lacks physical interpretability. This review synthesizes existing research to present a functional classification framework for MODFLOW–ANN integration, providing a systematic analysis of the literature within this structure. Our analysis of the literature, sourced from Scopus, Web of Science, and Google Scholar reveals a clear trend of the strategic integration of these models, representing a new trend in hydrogeological simulation. The literature reveals a classification framework that categorizes the primary integration strategies into three distinct approaches: (1) training an ANN on MODFLOW model outputs to create computationally efficient surrogate models; (2) using an ANN to estimate physical parameters for improved MODFLOW model calibration; and (3) applying ANNs as post-processors to correct systematic errors in MODFLOW model simulations. Our analysis reveals that these hybrid methods consistently outperform standalone approaches by leveraging ANNs for computational acceleration through surrogate modeling, for enhanced model calibration via intelligent parameter estimation, and for improved accuracy through systematic error correction.

There are serious ecological and environmental risks associated with groundwater level decline, particularly in areas with little in situ monitoring. In order to monitor and assess the resilience and dependability of groundwater storage, this paper proposes a solid methodology that combines data from land surface models and satellite gravimetry. In particular, the GRACE Groundwater Drought Index (GGDI) is used to analyze the estimated groundwater storage anomalies (GWSA) from the Gravity Recovery and Climate Experiment (GRACE) and the Global Land Data Assimilation System (GLDAS). Aquifer resilience, or the likelihood of recovery after stress, and aquifer reliability, or the long-term probability of remaining in a satisfactory state, are calculated using the core method. The two main components of the methodology are (a) calculating GWSA by subtracting the surface and soil moisture components from GLDAS, total water storage from GRACE, and comparing the results to in situ groundwater level data; and (b) standardizing GWSA time series to calculate GGDI and then estimating aquifer resilience and reliability based on predetermined threshold criteria. Using this framework, we validate GRACE-derived GWSA with in situ observations in eight sub-basins of the Chao Phraya River (CPR) basin, obtaining Pearson correlation coefficients greater than 0.82. With all sub-basins displaying values below 35%, the results raise significant questions about resilience and dependability. This method offers a framework that can be applied to assessments of groundwater sustainability worldwide.