Floods pose a major threat to lives, infrastructure, and ecosystems, particularly in areas where extreme rainfall events are becoming increasingly frequent. With the patterns of climate change and urbanization advancing at a fast pace, most areas are now more exposed to floods than ever before. In this research, we examine over a century of rainfall data, from 1901 to 2024, for all Indian states to better understand flood-prone conditions. A Bidirectional Long Short-Term Memory (Bi-LSTM) model is used to learn the temporal dependencies of rainfall sequences and forecast flood potential from past trends. The model is trained and tested on performance metrics such as accuracy, precision, recall, and F1-score. The results demonstrate that the Bi-LSTM method captures rainfall’s complex spatio-temporal patterns with an accuracy of 96.2% and gives robust predictive performance in detecting high-risk areas for flooding.