In natural alluvial channels, the determination of the flow resistance constitutes a problem with additional complexity compared to rigid bed channels, due to the bed morphology transformations and the alterations of the flow properties caused by sediment transport. While there have been steps towards understanding the processes that contribute to flow resistance in an alluvial channel, a robust quantitative model with wide applicability remains elusive. Machine learning offers the ability to exploit available data and generate equations that accurately describe the problem by taking implicitly into account the contributing mechanisms that are difficult to be modeled. In this paper, four machine learning techniques are employed for the mean flow velocity prediction, separately for sand-bed and gravel-bed rivers, namely artificial neural networks, adaptive-network-based fuzzy inference system, symbolic regression based on genetic programming, and support vector regression. The derived models are robust and their results are superior to those of some widely used flow resistance formulae, which compute the mean flow velocity from similar independent hydraulic variables.