## 1 Introduction  
Groundwater, a crucial global hydric resource, profoundly impacts various aspects of human life, including agriculture, industrial development, and drinking water provision [1, 2, 3]. The ability to comprehend and forecast the fluctuations in groundwater level (GWL) is paramount for effective water resource planning and management [4]. However, conventional statistical methods in GWL predictive modelling have limitations, such as data dependency and complex calibration processes, requiring the use of advanced and more robust approaches like the application of Artificial Intelligence (AI) methods [5, 6].  
Advancements in technology and modern methods have enabled AI to significantly enhance the prediction, planning, and management of groundwater resources [7]. Scientific investigation in this field can help identify the best strategies for the conservation and optimal management of this resource in the face of current and future changes, including climate change [8]. Furthermore, these studies can contribute to developing new technologies for more accurate monitoring and measurement of groundwater tables, providing essential information for better decision-making policies [9]. Hence, groundwater research is of great scientific and technical value and is crucial in conserving groundwater resources and formulating appropriate management strategies [10].  
Over the past twenty-two years, AI has been used extensively to overcome the limitations of conventional statistical approaches in simulating GWL. Indeed, based on the compilation and analysis of several articles published between 2001 and 2023 (see references list), it is evident that different research groups are increasing interest in using various AI-based methods to produce GWL predictive models. However, it is also evident that recent studies are lacking in many specific countries (Figure 1). This could be due to the absence of demand for groundwater resources and limited knowledge about the potential and procedures required for implementing AI-based predictive modelling that may influence this trend.  
Figure 2 shows a growing tendency in recent years to use newer AI-based methods, such as Artificial Neural Networks (ANNs), Deep Learning (DL), Genetic Programming (GP), and their combination with Hybrid Algorithms for more accurate GWL studies.  
This work comprehensively reviews the AI-based methods most used in recent years to predict GWL. Its primary purpose is to highlight each method's capabilities, combinations, and critical considerations for improving accuracy/efficiency in GWL prediction models. It also presents potential solutions to the challenges associated with their use. Practically, this review can assist water science researchers, such as geographers, geologists, engineers, environmentalists, and other professionals, to make methodological choices when modelling GWL and managing the related natural resources. Additionally, this article evaluates Hybrid Algorithms that combine AI and statistical methods, examining their  
Figure 1: A global map showing the spatial distribution of scientific studies conducted between 2001 and 2023 using AI-based methods in GWL modelling. The number of articles published in each country is provided.  
applications and advantages in geographic territories with limited environmental data. Ultimately, this review promotes awareness of advanced AI-based methods in groundwater management, emphasising practical applications and future potential.