This project uses hive sound recordings and machine learning to detect early signs of bee swarming. By identifying acoustic differences between swarming and stable colonies, the system predicts swarming with 93% accuracy. This enables beekeepers to intervene early, prevent colony loss, and even create new healthy colonies.