Flood susceptibility modeling in the watershed is to reduce the damages caused by the flood. The current study includes two tree-based algorithms for flood susceptibility analysis, namely, Classification And Regression Trees (CART) and Chi-squared Automatic Interaction Detection (CHAID). A flood inventory map was prepared by interpreting the past year’s data. Flood influencing factors like altitude, aspect, curvature, distance from river, drainage density, Land Use and Land Cover (LULC), Normalized Difference Vegetation Index (NDVI), Stream Power Index (SPI), slope, Sediment Transport Index (STI), Topographic Roughness Index (TRI) and Topographic Wetness Index (TWI) are considered here for model development. A flood susceptibility map is prepared by interpreting the flood inventory and influencing factors using these models for the Cachar district. To ensure the model accuracy Area under Curve method was used, and the curve shows that the AUC values are 0.922 and 0.900 for the validation dataset of the CHAID and CART model, respectively. Altitude and NDVI are the most crucial influencing factors in flood susceptibility mapping. This study indicates that the built-up area of the watershed is exposed to very high flood susceptibility. Therefore, steps must be taken for the remediation of floods.