Flow regimes – the primary governing force in riverine ecosystems – are closely linked to the life histories of organisms. Understanding how these life histories connect to flow regimes is crucial for developing transferable flow-ecology relationships that could enhance conservation efforts, such as the prescription of designer flows. However, data challenges often hinder the characterisation of such relationships. Using data from 109 sites across the Continental US for 184 riverine fish species, we characterised model-based riverine fish-flow response guilds through a heuristic optimisation algorithm — simulated annealing. Additionally, we analysed how species cluster based on four life history traits. We compared this clustering against the fish-flow response guilds to assess if they are associated with life histories. Our initial results indicate that the original 184 riverine fish species can be clustered into 28 distinct fish-flow response guilds. These findings suggest we can optimise the available datasets using a numerical algorithm into data-driven model-based groups. Preliminary results hint that life histories contribute to some differentiation among groups, although not all groups are distinctly separated. Group overlap may imply that other traits and mechanisms are underpinning these groupings. Although we anticipated that life histories would be the principal driver for the shared responses of fish species to flow regimes, these results emphasise the need to develop methods for optimally utilising available data. Comprehending the mechanisms underlying fish-flow ecology relationships will help create effective data-driven guilds, which will aid in the transferability of flow-ecology relationships into data-poor environments.