The livestock industry faces continuous pressure to enhance efficiency while encountering severe labor shortages and an increasing need for precise health management. Traditional methods for monitoring swine growth and behavior rely heavily on manual observation, which is not only labor-intensive but also susceptible to human error. To address this challenge, this paper proposes a Smart Swine Farming framework that integrates Arti-ficial Intelligence (AI) vision and Internet of Things (IoT) technologies. The framework comprises two core functional modules: a non-contact swine body length estimation module and a dynamic swine activity tracking module. In the swine body length estimation stage, this study utilizes YOLOv11-Pose for keypoint detection and integrates the MiDaS depth model to achieve swine body length calculation without physical contact. In the swine activity tracking stage, the system integrates YOLOv11 with the BoT-SORT tracker to continuously monitor swine behavior patterns, such as feeding, drinking, and corner dwelling. The core functionalities have been encapsulated as a RESTful API and visualized through Virtual Reality (VR) and a web interface, enabling swine farmers to intuitively access man-agement data. The expected outcomes are to significantly enhance the effec-tiveness of swine health management, optimize swine farming efficiency, and reduce labor resource investment for livestock producers.