Drought disasters signi cantly a ected human life and water resources. Therefore, forecasting methods like statistical models, machine learning, and deep learning architectures help scientists to take e ective decisions to decrease the e ects of natural disasters by providing decisionmaking plans. Droughts can be forecasted using meteorological indices like the standardized precipitation evapotranspiration index (SPEI), which aid governments in taking drought-prevention steps. In this paper, we present a big drought architecture for drought modeling and forecasting. The proposed architecture is composed of 5 layers: Data collection, data preprocessing, data storage, data processing and interpretation, and decision making. Besides, we present a comparative study between three di erent methods ARIMA, PROPHET, and LSTM for drought forecasting. Three di erent metrics are used for the performance evaluation Root Mean Squared Error (RMSE), coe cient of determination (R2), and Mean Squared Error (MAE). Experiments are carried out using data from the province of Jiangsu. Results revealed that LSTM outperformed the other models, and ARIMA outperformed the PROPHET model.