Skin diseases, encompassing approximately one-third of global human ailments, remain the fourth leading cause of global disease burden. Specifically, Rosacea, despite its significant prevalence, suffers from a marked scarcity in clinical visual data. Data scarcity, impedes the effective utilisation of deep learning models in computer-aided skin disease diagnosis, especially for conditions often overlooked in clinical visual/image data acquisition. This study meticulously addresses Rosacea’s data scarcity issue. An exhaustive literature survey spotlighted Synthetic Visual Data Generation as a potential solution to this data deficit. The central aim is to innovatively acquire and process data, mitigating the ramifications of visual data inadequacy by producing high fidelity synthetic visual data across three Rosacea subtypes. To the best of our knowledge, this constitutes the first attempt to employ Generative Adversarial Networks (GANs) with such a limited dataset of 300 images-a scenario in which GAN models typically struggle to converge. However, leveraging the theoretical principles of GANs enabled successful model convergence and the generation of high-fidelity Rosacea images using a variant of StyleGAN2. Furthermore, we have, for the first time, innovatively employed the concept of 3D Parametric Modelling and computer graphics, facilitating the construction of 3D head models for subtype-3 using only 268 images. The application of these techniques successfully generated synthetic data for Rosacea Subtypes-1, 2, and 3. For subtype-1 and 2, Board-certified expert dermatologists and lay participants validated the synthesised images. For Subtype-3, the efficacy of synthetic data was further corroborated by classification models, emphasising the viability of synthetic data when juxtaposed with real-world images. Grad-CAM visualisations provided additional validation of these models’ robustness. The resultant high-fidelity datasets for Rosacea Subtypes-1, 2, and 3, now publicly accessible, affirm the proficiency of synthetic image generation in addressing the challenges of data scarcity inherent to conditions like Rosacea.