Droughts are one of the most complex, common, and catastrophic natural disasters, causing severe damage to agriculture and the economy. However, drought susceptibility must be measured and predicted in a systematic way, especially in light of potential climate change scenarios. This study aimed to predict current and future drought susceptibility in Bangladesh using historical climate data (1991-2020) and coupled model intercomparison project 6 data for three seasons: pre-monsoon, monsoon, and post-monsoon. We applied an advanced machine-learning algorithm of artificial neural network (ANN) with a genetic algorithm (GA) optimizer to predict drought-prone areas. Nine hydrological parameters-rainfall, temperature, humidity, cloud coverage, wind speed, sunshine, potential evapotranspiration, and solar radiation-were used to develop drought susceptibility maps. Receiver operating characteristic curves and statistical metrics were used to validate the models. The results of a multilayer perceptron ANN coupled with a GA-based optimizer showed that the relevant statistical measures for training and testing datasets were the root mean square error (RMSE = 0.127 and 0.160) and coefficient of determination (R2 = 0.967 and 0.949) for the pre-monsoon season, monsoon season (RMSE = 0.023 and 0.035; R2 = 0.998 and 0.997), and post-monsoon season (RMSE = 0.083 and 0.142; R2 = 0.986 and 0.959), respectively. Further, drought-prone areas in the baseline drought period of 2020 for pre-monsoon season represented 23.86%, 14.24%, 12.85%, 29.92%, and 19.13% of the total area, respectively; similarly, for monsoon corresponding values were 1.83%, 44.18%, 4.99%, 8.76%, and 40.24%; and for post-monsoon drought they were 24.43%, 20.94%, 16.04%, 37.79%, and 0.80% of the total landmass of Bangladesh. These results can help reduce future drought impacts and be of value in assisting policy responses in the country.