Mouth breathing has been linked to a variety of negative health outcomes, including sleep-related disorders and dental problems. Detecting mouth breathing in the daily environment could be helpful for early intervention and reversing the negative impact. However, existing research has not adequately explored methods for detecting mouth breathing in everyday settings. This study presents a machine-learning approach using audio captured by commercially available earbuds to detect mouth breathing. By leveraging the growing popularity of earbuds for health monitoring, this approach offers a more convenient and non-invasive means of detecting mouth breathing. We conducted a data collection study with 30 participants to train a convolutional neural network-based model, which achieved an accuracy of 78.4% in detecting mouth breathing. Our findings suggest that audio-based mouth breathing detection using earbuds could be a promising tool for early intervention and improved health outcomes.