This review critically examines the application of Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) networks, and Gated Recurrent Units (GRUs) in hydrological time series predictions. The study highlights the strengths and limitations of these models, emphasizing their effectiveness in capturing complex temporal dependencies and non-linear relationships in hydrological data. RNNs, while foundational, face challenges such as vanishing gradients, which hinder their ability to model long-term dependencies. LSTMs and GRUs were developed to overcome these limitations, with LSTMs using memory cells and gating mechanisms, while GRUs provide a more streamlined architecture with similar benefits. The integration of attention mechanisms and hybrid models combining RNNs, LSTMs, and GRUs with other machine learning and deep learning techniques has improved prediction accuracy by capturing both temporal and spatial dependencies. Despite their effectiveness, practical implementations require extensive datasets and substantial computational resources. Future research should focus on developing interpretable architectures, enhancing data quality, incorporating domain knowledge, and utilizing transfer learning to improve model generalization and scalability. RNNs, LSTMs, and GRUs have been widely applied in hydrological tasks such as streamflow prediction, rainfall forecasting, and water quality monitoring. For example, LSTMs have been used to predict streamflow and rainfall with significant accuracy, while GRUs have shown promise in real-time applications. However, each model has limitations, such as RNNs' vulnerability to vanishing gradients, LSTMs' computational intensity, and GRUs' potential struggles with long-term dependencies. The effectiveness of these models varies depending on the application context, with each model exhibiting specific advantages and limitations that must be considered for optimal hydrological modeling outcomes.This review critically examines the application of Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) networks, and Gated Recurrent Units (GRUs) in hydrological time series predictions. The study highlights the strengths and limitations of these models, emphasizing their effectiveness in capturing complex temporal dependencies and non-linear relationships in hydrological data. RNNs, while foundational, face challenges such as vanishing gradients, which hinder their ability to model long-term dependencies. LSTMs and GRUs were developed to overcome these limitations, with LSTMs using memory cells and gating mechanisms, while GRUs provide a more streamlined architecture with similar benefits. The integration of attention mechanisms and hybrid models combining RNNs, LSTMs, and GRUs with other machine learning and deep learning techniques has improved prediction accuracy by capturing both temporal and spatial dependencies. Despite their effectiveness, practical implementations require extensive datasets and substantial computational resources. Future research should focus on developing interpretable architectures, enhancing data quality, incorporating domain knowledge, and utilizing transfer learning to improve model generalization and scalability. RNNs, LSTMs, and GRUs have been widely applied in hydrological tasks such as streamflow prediction, rainfall forecasting, and water quality monitoring. For example, LSTMs have been used to predict streamflow and rainfall with significant accuracy, while GRUs have shown promise in real-time applications. However, each model has limitations, such as RNNs' vulnerability to vanishing gradients, LSTMs' computational intensity, and GRUs' potential struggles with long-term dependencies. The effectiveness of these models varies depending on the application context, with each model exhibiting specific advantages and limitations that must be considered for optimal hydrological modeling outcomes.