Purpose: To examine the reporting and influence of dataset quantity and quality in generative adversarial network (GAN)-based dental prosthetic generation and to summarize the evidence available through 16 July 2026. Material/Methods: A structured literature search was conducted in PubMed, Scopus, Web of Science, IEEE Xplore, SpringerLink, arXiv, and Google Scholar. Fifteen original GAN or explicitly adversarial studies of prosthetic generation or validation were included in the focused qualitative synthesis. Nine non-GAN generative or automated artificial-intelligence studies published in 2025-2026 were analyzed separately as contextual evidence. Results: Dataset definitions ranged from small three-dimensional collections to more than 1500 depth-map cases. ToothGAN used 814 natural-tooth and technician-designed cases and showed that the dataset composition affected anatomical features, groove depth, and occlusal contact characteristics. A 3D GAN implant-crown study demonstrated that overlap metrics should be interpreted together with clinically relevant emergence-profile parameters. Contextual transformer, point-cloud, diffusion, and commercial-system studies reported increasingly explicit data splits and clinical endpoints, although several proprietary systems did not disclose their training data. Conclusions: No universal minimum dataset size can be established. Dataset composition, anatomical diversity, ground-truth definition, resolution, augmentation, independent testing, and clinical validation are at least as important as sample count.