Simple SummaryThis paper provides a review of recent studies exploring the application of artificial intelligence (AI) in the early detection and monitoring of respiratory disease in swine, emphasizing the significance of early detection for preventing economic losses. The studies primarily focus on utilizing coughing sounds as a feature in disease recognition, comparing different AI models and methodologies. A commercially available AI system that integrates temperature and humidity sensors with audio technologies for respiratory health monitoring through cough-sound identification is also assessed. However, the limitations of the current technology are identified, highlighting the need for further advancements to develop smarter AI solutions for swine respiratory health monitoring.AbstractPorcine respiratory disease complex is an economically important disease in the swine industry. Early detection of the disease is crucial for immediate response to the disease at the farm level to prevent and minimize the potential damage that it may cause. In this paper, recent studies on the application of artificial intelligence (AI) in the early detection and monitoring of respiratory disease in swine have been reviewed. Most of the studies used coughing sounds as a feature of respiratory disease. The performance of different models and the methodologies used for cough recognition using AI were reviewed and compared. An AI technology available in the market was also reviewed. The device uses audio technology that can monitor and evaluate the herd’s respiratory health status through cough-sound recognition and quantification. The device also has temperature and humidity sensors to monitor environmental conditions. It has an alarm system based on variations in coughing patterns and abrupt temperature changes. However, some limitations of the existing technology were identified. Substantial effort must be exerted to surmount the limitations to have a smarter AI technology for monitoring respiratory health status in swine.