Early detection of monkeypox is thus crucial to containing its spread and reducing public health risks. Proper identification of monkeypox strain may be difficult owing to its close similarities with other pox virus strains, a requirement thus for innovative solutions for accurate and timely detection. This research seeks to create A system for the early detection of monkeypox disease using the strong deep learning methods based on the use of the state-of-the-art Generative Adversarial Network (GAN) and VGG19 model. The framework presented here uses the GAN model in creating the augmented data samples for the creation of this, addressing the lack of data. The VGG19 model is used for the diagnosis of these augmented High-accuracy data samples and monkeypox robustness detection. Due to the integration of the present model, such diagnostic mistakes can be minimized and the sensitivity and specificity of the system are enhanced. The experiments conducted based on publicly available medical datasets illustrate that GAN-VGG19 combination will provide a greater improvement in classification performance and accuracy rate as high as 97%. Thus, this model posits the possibility of deep learning approaches towards propelling early and accurate diagnosis of monkeypox for prompt interventions with improved public health impacts.