Skin diseases constitute of a set of diseases that emerge as either acute or chronic skin issues ranging from the common rashes (eczema and acne) to severe skin conditions as psoriasis and skin cancer. These are disease conditions and they exhibit skin rashes, ulcers, discoloration, and textural degeneration that require an early detection and a correct diagnosis in order to take faster control of the situation. This work focuses on ensuring a deep learning framework implemented through conditioned generative adversarial networks (cGANs) to identify skin diseases using datasets like HAM (Human Against Machine) dataset that include disease images of the skin. Our suggested model considers crucial parameters such as gender, age, and labels to expand and diversify the dataset for more accuracy. The effectiveness of the cGAN in accurately identifying particular skin diseases is investigated via a process that fully exploits rigorous testing and evaluation. The outcomes are extremely encouraging in regard to the early detection, which if applied could change the existing standard of care in dermatology that is based on clinical diagnostics and therapeutic approaches.