The demand for water resources has increased significantly, especially with the continuous growth of the population. According to the United Nations, the global population has more than tripled compared to what it was in the mid-20th century. This situation has required countries worldwide to seek solutions to develop and enhance their surface and groundwater resources (building dams, artificial recharge of aquifers, seawater desalination plants, etc.). In recent years, a number of scientific studies have shown that artificial intelligence can help ensure the sustainability of water resources through predictions and identifying factors that affect the decline of water resources in a studied area. This review paper examines and discuss previous research related to the applications of AI-based techniques to improve hydrological modeling and the comparison with traditional models. The comparison will focus on identifying the strengths and limitations of different approaches, evaluating the accuracy and efficiency of predictive models, and contrasting the outcomes across various geographic regions and environmental conditions. This will provide insights into areas for future improvement and highlight the prospects for the development of AI-driven water management solutions.