Time-series prediction problems have been effectively solved by deep neural networks lately given their ability to understand temporal characteristics found in time series. In this study, a deep learning-based flood occurrence prediction method is presented for the successful interpretation of weather events and meteorological data with higher accuracy. The proposed model is evaluated on the United States National Climate Data Center (NCDC) dataset, NCDC storm events. Correlation analysis was performed on the meteorological and weather phenomenal parameters for choosing the appropriate parameters. The experimental results show that the model achieves 87.8% accuracy while predicting floods in the United States from the year 2013 to 2019.