Skin diseases range from mild conditions to severe threats, such as melanoma highlighting the critical importance of early and accurate evaluation for effective treatment. Traditional diagnostic methods that depend heavily on visual inspection and biopsy are often prone to delays and susceptible to human error underscoring the need for more efficient and reliable approaches. This study introduced a computerized system for detecting skin diseases using deep learning techniques and classification, with a focus on enhancing diagnostic efficiency and accuracy. The approach begins with the comprehensive preprocessing of skin images including illumination adjustment, elimination of artifacts via morphology closing and edge improvement using an unsharp filter. These steps improve the image clarity and prepare the data for accurate analysis. To address variations in lesion size and boundary irregularities a Fuzzy k-means clustering technique segments the affected skin regions, ensuring adaptable detection across diverse skin conditions. The classifier Deep-MobileNet integrates the lightweight MobileNet architecture with Squeeze-and-Excitation (SE) blocks enabling it to effectively differentiate between healthy and diseased skin. SE blocks enhance the model’s ability to capture spatial dependencies thereby improving classification precision. To further optimize the classifier’s performance hyperparameters are tuned using the adaptive deer hunting optimization (ADHO) algorithm, which accelerates convergence and boosts model efficiency. By providing an automated efficient solution this approach has the potential to assist healthcare providers in diagnosing skin diseases quickly and reliably thereby supporting timely and effective treatment interventions. The efficiency of the suggested method is evaluated based on accuracy, sensitivity, specificity and F1-Score. The experimental outcome showed that recommended approach attained a maximum accuracy of 91.96%.