This study proposes a data-driven Cyber-Physical System (CPS) for oral health monitoring that embeds an innovative deep learning model. By integrating sensor data from the physical world with artificial intelligence (AI) algorithms from the cyber world, the system establishes a closed loop from data collection and diagnostic inference to feedback-driven decision-making. The novel deep learning model, which is Composite Attention Backbone Segmentation Network (CBASNet), is designed to analyze oral panoramic X-ray images, enabling multi-pathology diagnosis and the identification of restorative methods. Experimental results demonstrate that the proposed system excels in oral health monitoring and disease diagnosis, achieving state-of-the-art performance with 91.9% Bbox mAP, 88.4% Segm mAP, and 87.7% Dice, surpassing traditional diagnostic methods and existing technologies. These results highlight the system's ability to improve the early detection rate of oral diseases, enhance treatment outcomes, and provide an innovative solution for intelligent diagnosis in dentistry.