Extreme flood hazards are common in the Lower Colorado River basin due to the complex terrain and entrenched river channels. Evaluating basin morphometry helps understand the physical behavior of watersheds with respect to extreme floods events. However, extracting basin morphometric characteristics is computationally expensive and time consuming. Conventional approaches lack effective tools that link morphometric indices to extreme floods, and this poses a great challenge for extreme flood prediction. In this study, we extracted 41 basin morphometric parameters for 372 watersheds in the Lower Colorado River Basin from a 10 m DEM using ArcGIS with Python script. We then employed the Random Forest (RF) regression with the GridSerachCV algorithm and Out-of-Bag (OOB) error estimation to link these morphometric features to the floods-of-record. The results indicate that the RF model has a better estimation to peak discharge per unit area (UP) than maximum annual peak discharge (MAP). The results also suggest that significant improvement in predicting the MAP is achieved with the relative basin perimeter, total basin area, and length area relation. Similar improvement in predicting UP is achieved using the maximum height of basin, total basin relief, and relief ratio. This initial effort using RF shows that data-driven machine learning can help link basin morphometry to measures of extreme flooding, thereby advancing our understanding of regional large flood behavior and improving flood risk analyses for the Southwestern U.S.