Driven by the demand for prompt diagnosis, this study integrates artificial intelligence into dental aesthetics, with a focus on smile analysis. The aim is to confront prevalent challenges such as latency, subjectivity, and the lack of smart diagnostic tools in dental aesthetics. Enabled by the advancements in generative artificial intelligence (GenAI), neural networks, and the Internet of Things, we propose a framework to detect smile aesthetic issues such as excessive gingival display (i.e. a high lip line that exposes 2-3 mm of gingiva above the crown of the maxillary teeth) and smile arc classification. Despite recent improvements in deep learning within computer vision, the dental sector contains a gap of publicly available datasets. To mitigate overfitting risks, our approach incorporates GenAI data augmentation to improve convolutional neural network (CNN) performance. We leverage a diffusion model to generate a text-to-image dataset utilized across training, validation, and testing phases in different proportions. Notably, the GenAI CNN model exhibits an impressive accuracy of 81.607% in detecting excessive gingival display, surpassing established baselines. The development of a fully connected pipeline from the CNN model to the user interface facilitates timely interactions between patients and clinics. This framework unveils the potential of a visionary Internet of Mirrors, offering a precise diagnostic tool and fostering enhanced communication between users and healthcare providers.