This review provides a comprehensive analysis of deep learning (DL) methods for hydrological forecasting. The paper discusses the application of convolutional neural networks (CNNs), recurrent neural networks (RNNs), long short-term memory (LSTM), gated recurrent units (GRUs), generative adversarial networks (GANs), residual networks (ResNets), and graph neural networks (GNNs) in hydrological forecasting. It evaluates their performance in terms of prediction accuracy, computational costs, and practical utility in engineering applications. The study highlights the benefits and challenges of using DL techniques for hydrological forecasting, including the need for high-quality data, computational resources, and model interpretability. CNNs are effective for image-based analysis, such as urban flood warning systems, but perform poorly in time-series prediction. RNNs, particularly LSTM and GRU, are well-suited for time-series forecasting due to their ability to capture long-term dependencies. LSTM models have shown superior performance in hydrological forecasting, especially in capturing complex patterns in rainfall-runoff processes. Improved LSTM models, such as residual LSTM (ResLSTM) and LSTM with particle swarm optimization (PSO-LSTM), have demonstrated enhanced accuracy and robustness in flood prediction. GRUs, which are simpler than LSTMs, also show promising results in hydrological forecasting. GANs have been applied to address the issue of incomplete hydrological data by generating synthetic data that can be used for training models. ResNets and GNNs have also been explored for their ability to handle complex spatial and temporal patterns in hydrological data. The integration of DL models with physical models has shown potential in improving prediction accuracy and robustness, although it requires domain expertise and computational resources. The review emphasizes the importance of data quality, model interpretability, and computational efficiency in hydrological forecasting. It also highlights the need for further research to address the challenges of long-term forecasting, spatial autocorrelation, and the integration of DL models with physical models. Overall, DL techniques offer significant potential for improving the accuracy and reliability of hydrological forecasting, but their application requires careful consideration of the specific challenges and requirements of each forecasting task.This review provides a comprehensive analysis of deep learning (DL) methods for hydrological forecasting. The paper discusses the application of convolutional neural networks (CNNs), recurrent neural networks (RNNs), long short-term memory (LSTM), gated recurrent units (GRUs), generative adversarial networks (GANs), residual networks (ResNets), and graph neural networks (GNNs) in hydrological forecasting. It evaluates their performance in terms of prediction accuracy, computational costs, and practical utility in engineering applications. The study highlights the benefits and challenges of using DL techniques for hydrological forecasting, including the need for high-quality data, computational resources, and model interpretability. CNNs are effective for image-based analysis, such as urban flood warning systems, but perform poorly in time-series prediction. RNNs, particularly LSTM and GRU, are well-suited for time-series forecasting due to their ability to capture long-term dependencies. LSTM models have shown superior performance in hydrological forecasting, especially in capturing complex patterns in rainfall-runoff processes. Improved LSTM models, such as residual LSTM (ResLSTM) and LSTM with particle swarm optimization (PSO-LSTM), have demonstrated enhanced accuracy and robustness in flood prediction. GRUs, which are simpler than LSTMs, also show promising results in hydrological forecasting. GANs have been applied to address the issue of incomplete hydrological data by generating synthetic data that can be used for training models. ResNets and GNNs have also been explored for their ability to handle complex spatial and temporal patterns in hydrological data. The integration of DL models with physical models has shown potential in improving prediction accuracy and robustness, although it requires domain expertise and computational resources. The review emphasizes the importance of data quality, model interpretability, and computational efficiency in hydrological forecasting. It also highlights the need for further research to address the challenges of long-term forecasting, spatial autocorrelation, and the integration of DL models with physical models. Overall, DL techniques offer significant potential for improving the accuracy and reliability of hydrological forecasting, but their application requires careful consideration of the specific challenges and requirements of each forecasting task.