This review explores the application of machine learning in water resource modeling, focusing on predicting various hydrological factors such as precipitation, flood, runoff, soil moisture, evapotranspiration, groundwater level, and water quality. Traditional hydrological models are limited by their complexity and reliance on physical principles, while machine learning offers more efficient and accurate predictions by analyzing large datasets. The review summarizes various machine learning algorithms, their strengths and weaknesses, and their potential applications in water resource modeling. It discusses the advantages and challenges of different machine learning methods, including supervised and unsupervised learning, and highlights the importance of selecting appropriate models for specific tasks. The review also addresses the need for further research to improve the accuracy and efficiency of machine learning in water resource modeling. Key findings include the effectiveness of machine learning in predicting precipitation, flood, runoff, soil moisture, evapotranspiration, and groundwater levels, as well as the challenges in applying these models to real-world scenarios. The review emphasizes the importance of integrating machine learning with physical models and remote sensing data to enhance the accuracy and reliability of water resource predictions. Overall, the review highlights the potential of machine learning in advancing water resource management and protection.This review explores the application of machine learning in water resource modeling, focusing on predicting various hydrological factors such as precipitation, flood, runoff, soil moisture, evapotranspiration, groundwater level, and water quality. Traditional hydrological models are limited by their complexity and reliance on physical principles, while machine learning offers more efficient and accurate predictions by analyzing large datasets. The review summarizes various machine learning algorithms, their strengths and weaknesses, and their potential applications in water resource modeling. It discusses the advantages and challenges of different machine learning methods, including supervised and unsupervised learning, and highlights the importance of selecting appropriate models for specific tasks. The review also addresses the need for further research to improve the accuracy and efficiency of machine learning in water resource modeling. Key findings include the effectiveness of machine learning in predicting precipitation, flood, runoff, soil moisture, evapotranspiration, and groundwater levels, as well as the challenges in applying these models to real-world scenarios. The review emphasizes the importance of integrating machine learning with physical models and remote sensing data to enhance the accuracy and reliability of water resource predictions. Overall, the review highlights the potential of machine learning in advancing water resource management and protection.