###### Abstract
The comparison discussed in this review aims to evaluate the performance [10] of different machine learning (ML) [11; 12; 13; 14] and deep learning models in predicting groundwater level (GWL) [15; 16; 17; 18]. The groundwater level is an essential indicator of the availability of freshwater resources and is closely related to various hydrological and ecological processes. Therefore, accurate groundwater level prediction is crucial for sustainable water management and resource allocation. Machine learning is a branch of artificial intelligence that focuses on developing algorithms to learn patterns from data and make predictions based on that knowledge. There are various types of machine learning models, including decision trees [19; 20; 21], random forests [22; 23; 24; 25], support vector machines (SVM) [26], and artificial neural networks (ANN). On the other hand, deep learning is a subset of machine learning that focuses on developing artificial neural networks with multiple hidden layers. These deep neural networks can learn complex patterns and relationships in data, making them particularly useful for tasks such as image recognition, natural language processing, and prediction modeling. Consequently, different machine learning and deep learning models are applied to predict groundwater levels and their performance is compared. The comparison is based on various evaluation metrics, such as accuracy, precision, recall [27; 28], and mean absolute error (MAE), R\({}^{2}\)[29]. The comparison results provide insight into the strengths and weaknesses of different models and can help researchers and practitioners choose the most appropriate model for their specific application. Overall, comparing groundwater level prediction modeling using different machine learning and deep learning models provides valuable information for researchers and practitioners working in hydrology and water resources management. In this paper, a collection of new theories for developing and designing a novel methodology and improving modeling efficiency are also considered in the appropriate field of evaluation. They examine modeling techniques used in all the reviewed studies; it was estimated that the machine learning and deep learning approaches are efficient enough for modeling GWL. The primary purpose of this paper is to focus on the following research question: how is GWL predicted? The recent research refers to the different stages of groundwater level prediction. In every step, the methods discussed in the reviewed studies are analyzed and compared based on their benefits and drawbacks. A new model is proposed in this study to simulate yearly GWL using wavelet Bidirectional-LSTM (W-Bi-LSTM). The structure of the paper is as follows: Section 2 goes over the methodology of the research, Section 3 presents the groundwater and surface water data sources and availability. Section 4 illustrates the conventional, ML-, and deep-learning-based groundwater level prediction techniques. Section 5 briefly discusses the performance evaluation of different models and Section 6 represents the future research direction and discussion. Finally, Section 7 ends the paper with a conclusion.