## 1 Introduction  
Groundwater is one of the most important sources of water [1]. Groundwater provides drinking water to up to 50% of the world's population [2], as well as accounting for 43% of all agricultural water [3]. Groundwater is a major natural resource for our mother earth, accounting for around 95 percent of all freshwater on the planet [4], making it essential for human existence and economic advancement. The effects of global climate change on groundwater level changes are significant enough to warrant further investigation in order to enhance forecasting and future consequences [5]. Due to poor extraction and overexploitation, the volume of groundwater has declined in recent years, worsening future water shortages [6]. The need to study these important resources is vital to ensure sustainable development. Researchers have used a variety of machine learning (ML) models to predict groundwater level (GWL) changes, including [7], who used a hybrid ML model, ref. [8] who used an ensemble modeling framework based on spectral analysis, machine learning, and uncertainty analysis, ref. [9] who used two ANN models, ref. [10] who used random forest (RF), and [11], who used two commonly employed machine learning models: multi-linear regression (MLR) and random forest (RF). In addition, statistical models (SM) by [12; 13; 14] and mathematical models (MM) by [15; 16; 17] have also been used to predict GWL changes. In recent years, there has been a growing interest in applying machine learning and data-driven methodologies to groundwater modeling [18; 19; 20]. With the persistent threat of climate change and human influences, access to high-resolution and continuous hydrologic data is critical for projecting trends and water resource availability [19]. Many studies on groundwater level measurement have been conducted due to the relevance of assessing and forecasting groundwater level changes [20]. Predicting the level of groundwater in farms with accuracy and simplicity is an important part of agricultural water management [21; 22]. The use of groundwater for various activities has increased in the last decades [8]. Machine learning and mathematical modeling methodologies and techniques [23] have been used to forecast changes in groundwater levels based on research [24; 25]. In addition, it must be noted that the state of the water as well as its level are interesting aspects of groundwater modeling (e.g., concentrations of different chemicals and geospatial analysis) [26; 27]. However, the combination of mathematical and machine learning approaches to forecast groundwater level changes is scarce. Although previous studies have summarized and addressed either ML or MM on GWL, none have, to our knowledge, focused on both ML and MM models on groundwater level fluctuations in a systematic means. The goal of this study is to offer a thorough evaluation of existing research on the application of machine learning, as well as mathematical and statistical methodologies, for modeling and forecasting groundwater resources. By conducting a detailed analysis of the gathered data in a systematic review, this work explores the applicability of ML, MM, and SM techniques for estimating groundwater level resources. Monitoring groundwater levels helps to ensure that the aquifer systems in the basin are well understood, as well as how they react to changes in groundwater recharge, pumping, and other factors [26]. The estimation of groundwater level variations can also be used to determine how much groundwater storage has increased as a result of recharge or decreased as a result of discharge, such as extraction for usage [27]. For effective groundwater resource development, quantification of groundwater level fluctuations is a fundamental prerequisite, and this is especially important for areas with a preponderance of semi-arid and arid environments.