The flood is the most frequent disaster hitting the globe. The mid and low latitudinal regions and low altitudinal regions are prone to monsoonal and cyclonal floods. The Gangetic Plain is embedded with the net of rivers and streams; hence, floods are very common hazards of the region. Banda District of Uttar Pradesh lying in the flood plain of Gangetic River system is an agriculturally dominated district. The frequent floods are highly damaging to the agricultural fields. Therefore, it is of utmost importance that the flood susceptibility of the region is modeled. The flood inventory data is highly useful for precise modeling of floods in association of geographic information system (GIS) techniques and machine learning algorithms. In this study, GIS methods and machine learning algorithm, i.e., Boosted Regression Tree (BRT), are adopted for predictive modeling of flood susceptibility of the district and impact is assessed on the agricultural area in the year 2021. Different zones of flood susceptibility have been classified and agricultural area falling in those zones is analyzed. About 91.40% of low flood susceptibility area is agricultural, while 79.17% of the high flood susceptibility area is agricultural.