Forecasting river flow is crucial for optimal planning, management, and sustainability using freshwater resources. Many machine learning (ML) approaches have been enhanced to improve streamflow prediction. Hybrid techniques have been viewed as a viable method for enhancing the accuracy of univariate streamflow estimation when compared to standalone approaches. Current researchers have also emphasised using hybrid models to improve forecast accuracy. Accordingly, this paper conducts an updated literature review of applications of hybrid models in estimating streamflow over the last five years, summarising data preprocessing, univariate machine learning modelling strategy, advantages and disadvantages of standalone ML techniques, hybrid models, and performance metrics. This study focuses on two types of hybrid models: parameter optimisation-based hybrid models (OBH) and hybridisation of parameter optimisation-based and preprocessing-based hybrid models (HOPH). Overall, this research supports the idea that meta-heuristic approaches precisely improve ML techniques. It's also one of the first efforts to comprehensively examine the efficiency of various meta-heuristic approaches (classified into four primary classes) hybridised with ML techniques. This study revealed that previous research applied swarm, evolutionary, physics, and hybrid metaheuristics with 77%, 61%, 12%, and 12%, respectively. Finally, there is still room for improving OBH and HOPH models by examining different data pre-processing techniques and metaheuristic algorithms.