Timely and precise identification of skin conditions is essential for patient outcomes and efficient treatment. Conventional techniques frequently depend on dermatologists’ subjective and time-consuming visual evaluation. This chapter introduces a deep learning-based multi-class skin disease diagnosis model. The proposed model supports Sustainable Development Goal 3 by improving access to accurate and efficient skin disease diagnoses, thereby enhancing overall health and timely treatment. Image classification and data preprocessing phases make up the suggested model. Data preprocessing incorporates image resizing for uniformity and data augmentation techniques to improve the ability of the model to manage variances in images. Image classification phase builds upon a pre-trained ResNet50 architecture, which efficiently extracts high-level features from input images. To enhance the model’s discrimination between five specific skin disease categories, the classification stage leverages additional processing layers to refine the high-level features extracted by the ResNet50 network, ultimately enhancing the model’s ability to discriminate between different disease categories. The evaluation of a comprehensive dataset demonstrates the model’s effectiveness. The suggested ResNet model attains 90.74% accuracy, 87.78% precision, 86.84% sensitivity, and 87.04% F1-score. The findings illustrate how competitive the suggested model is since it performs better than the state-of-the-art model currently in use.