Bangladesh experiences frequent hydro-climatic disasters such as flooding. These disasters are believed to be associated with land use changes and climate variability. However, identifying the factors that lead to flooding is challenging. This study mapped flood susceptibility in the northeast region of Bangladesh using Bayesian regularization back propagation（BRBP） neural network, classification and regression trees（CART）, a statistical model（STM） using the evidence belief function（EBF）, and their ensemble models（EMs） for three time periods（2000, 2014, and 2017）. The accuracy of machine learning algorithms（MLAs）, STM, and EMs were assessed by considering the area under the curve—receiver operating characteristic（AUC-ROC）. Evaluation of the accuracy levels of the aforementioned algorithms revealed that EM4（BRBP-CART-EBF） outperformed（AUC > 90%） standalone and other ensemble models for the three time periods analyzed. Furthermore, this study investigated the relationships among land cover change（LCC）, population growth（PG）, road density（RD）, and relative change of flooding（RCF） areas for the period between 2000 and 2017. The results showed that areas with very high susceptibility to flooding increased by 19.72% between 2000 and 2017, while the PG rate increased by 51.68% over the same period.The Pearson correlation coefficient for RCF and RD was calculated to be 0.496. These findings highlight the significant association between floods and causative factors. The study findings could be valuable to policymakers and resource managers as they can lead to improvements in flood management and reduction in flood damage and risks.