As the application of AI in healthcare grows, gaining trust in these technologies is critical, especially in the sensitive field of skin disease diagnosis. This study discusses the incorporation of AI in dermatological disease detection, with an emphasis on increasing confidence via accuracy. It presents a structure based on transparency, interpretability, fairness, robustness, and accountability. By utilising explainable AI methodologies, rigorous data preparation, and severe model validation, the strategy dramatically enhances interpretability, allowing clinicians to trust AI choices. Fairness measures are included to decrease biases across demographic groups, and accountability systems aid in mistake detection and model refining. Using a large dermatological dataset, the system shows significant gains in trustworthiness and diagnosis accuracy. Additionally, this research explores the efficacy of the MobileNet model in classifying multi-class skin diseases. MobileNet achieves 83.1% accuracy, outperforming dermatologists in several diagnostics and showing robust metrics with 89% precision, 83% recall, and 83% F1-score, underscoring its potential in real-time computer-aided diagnosis systems.