Swarming is a natural process that leads to reduced honey production and poses a challenge for beekeepers. Precision beekeeping provides swarm notifications to help prevent this phenomenon. The paper investigates the utilization of machine learning for the early detection of bee swarming behavior through the analysis of audio data. It employs three feature extraction methods—Mel Frequency Cepstral Coefficients (MFCC), Short-Time Fourier Transform (STFT), and Chroma—to capture important characteristics of bee sounds. The effectiveness of five machine learning models (K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Naive Bayes (NB), Random Forest (RF), and Gradient Boosting (GB)) is evaluated in distinguishing between swarming and non-swarming states using two real-world datasets collected in Vietnam. To ensure generalizability, the models are assessed on a separate validation set that is not used during training. The experiment results reveal the significant potential of employing machine learning methods for the detection of bee swarming.