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Abstract Despite forming under different flow conditions, the geometries of tidal and fluvial channel planforms and planform transformations display significant overlap, hindering efforts to differentiate them geometrically. Although studies have demonstrated that globally, tidal and fluvial planforms are statistically distinct based on meander metrics, there are currently no machine‐learning methodologies for classifying channels as tidal or fluvial that do not focus on meander‐specific geometries. In this study, we present a methodology for classifying channel planforms as tidal or fluvial using statistical representations of channel planforms and machine‐learning algorithms. Using a dataset of 4294 tidal and fluvial channel segments (63 channel reaches), we trained three machine‐learning classifiers (Logistic Regression, Multi‐layer Perceptron, and Random Forest) across 69 trials to identify the machine‐learning algorithm and variables that perform best at classifying channel reaches. We evaluated the performance of the classifiers at three thresholds based on the percent of channel segments correctly identified in a given reach (>50%, >66% and >75%). At the >50% classification threshold, all three classifiers attained a 95% reach‐scale accuracy during individual trials. However, at higher classification thresholds, the RF classifier performed best. Feature importances from the RF classifier indicate that measures of the central tendency and minimum/maximum of the normalized radius of curvature convolved with normalized width of channel segments play a key role in differentiating between the planforms, with normalized width also contributing to the difference. This indicates that the relationship between width and radius of curvature is more important than width or measures of curvature on their own. This result likely reflects the downstream funnelling of tidal channels and the limitation on the sharpness of bends associated with increased width. These methods have potential for application in the study of channels preserved on relict geomorphic surfaces and in mixed‐energy settings.