Estimation of design flood is a crucial task in water resources engineering. Regional Flood Frequency Analysis is one of the widely used approaches for estimating design flood in ungauged basin. In the present research, we develop an eXtreme Gradient Boost based ML model for RFFA. The proposed approach relies on developing a regression model between flood quantiles and the commonly available catchment descriptors. In this study, the CAMELs data for 671 catchments from USA was used to study the efficiency of the approach. Further, the results were compared with the traditional methods such Multiple Linear Regression (MLR) and Artificial Neural Networks (ANN). XGB is a decision-tree-based ensemble machine learning algorithm that uses gradient boosting as a framework. The results revealed that the XGB based approach resulted in estimates with highest accuracy when using all the available catchment descriptors (i.e., mean annual rainfall(MAR), drainage area, fraction forest, mean annual potential evapotranspiration (MAPET), mean annual temperature, rainfall intensity, slope, fraction snow, soil porosity, and soil conductivity) both during training and validation. Four distinct models consisting of three to ten descriptors were examined for 2-, 5-, 10-, 25-, 50-, and 100-year return periods, all of the models exhibit smaller mean absolute error values and root mean square error values with percentage bias ranging from -10 to +10. A model with three predictor variables has comparable performance to other models. Drainage area, rainfall intensity, MAR, and fraction snow are the most efficient predictor variables, while MAPET, Slope, Temperature, Fraction Forest, Soil Porosity, and Soil Conductivity have low significance in predicting design flood for an ungauged catchment. The XGB modeling approach that has been proposed can be applied to different places throughout the world.