In the context of skin diseases, early treatment is very important, especially in resource-limited settings. The paper presents a new approach for intelligent analysis of skin diseases using Social Crow Search Optimization and Recurrent Neural Networks. SCSO optimizes the hyperparameters of RNN for better classification accuracy and avoidance of computational complexity. The proposed framework was tested on a benchmark dermoscopic image dataset at an accuracy of 95.8%, sensitivity of 93.4%, and specificity of 96.7%, with 6% improvement over traditional optimization methods on average. Improving the quality of images enhances feature extraction, which further improves the performance of the model. This scalable and reliable solution holds much promise to be further developed in dermatological diagnostics for telemedicine and automated healthcare systems.