This review summarizes recent developments in hydrologic forecast merging (HFM) techniques since the early 1990s. Hydrologic forecasting is crucial for sustainable water resource management, especially for predicting extreme flow events. Due to inherent uncertainties in hydrologic models, merging multiple models or ensembles has become a key approach to improve forecast accuracy and reliability. HFM techniques combine forecasts from different models to enhance performance, reduce uncertainty, and increase confidence in predictions. Bayesian model averaging (BMA) is the most popular method due to its ability to reduce uncertainty and provide accurate forecasts in both deterministic and probabilistic simulations. Regression techniques are also robust and efficient, performing well across various merging applications. While specific techniques like model-dependent weighted averaging and neural networks effectively reduce forecast uncertainty, there is still room for improvement in forecast accuracy across different lead times. Future research should focus on advanced HFM techniques for estimating optimal weights in time-varying domains and overcoming limitations, such as simulating low flows in seasonally dry catchments. The review covers developments in flow simulation, uncertainty analysis, monthly and seasonal streamflow predictions, ensemble forecasts, flood forecasting, and climate change analysis. It highlights the strengths and weaknesses of different HFM techniques, emphasizing the importance of selecting appropriate methods based on specific applications and data characteristics. The review also discusses the practical applications of HFM techniques in various hydrological contexts, including flow simulation, uncertainty analysis, and ensemble forecasting. Overall, the review aims to provide a comprehensive understanding of HFM techniques, their advantages, limitations, and future research directions.This review summarizes recent developments in hydrologic forecast merging (HFM) techniques since the early 1990s. Hydrologic forecasting is crucial for sustainable water resource management, especially for predicting extreme flow events. Due to inherent uncertainties in hydrologic models, merging multiple models or ensembles has become a key approach to improve forecast accuracy and reliability. HFM techniques combine forecasts from different models to enhance performance, reduce uncertainty, and increase confidence in predictions. Bayesian model averaging (BMA) is the most popular method due to its ability to reduce uncertainty and provide accurate forecasts in both deterministic and probabilistic simulations. Regression techniques are also robust and efficient, performing well across various merging applications. While specific techniques like model-dependent weighted averaging and neural networks effectively reduce forecast uncertainty, there is still room for improvement in forecast accuracy across different lead times. Future research should focus on advanced HFM techniques for estimating optimal weights in time-varying domains and overcoming limitations, such as simulating low flows in seasonally dry catchments. The review covers developments in flow simulation, uncertainty analysis, monthly and seasonal streamflow predictions, ensemble forecasts, flood forecasting, and climate change analysis. It highlights the strengths and weaknesses of different HFM techniques, emphasizing the importance of selecting appropriate methods based on specific applications and data characteristics. The review also discusses the practical applications of HFM techniques in various hydrological contexts, including flow simulation, uncertainty analysis, and ensemble forecasting. Overall, the review aims to provide a comprehensive understanding of HFM techniques, their advantages, limitations, and future research directions.