In today’s world is difficult to accurately diagnose skin conditions, including skin malignancies, and this can result in improper therapy and misdiagnosis. Diagnostic accuracy is vital for better prediction and illness management, even if misdiagnoses have serious repercussions for individuals and the healthcare system, including postponed therapies, disease progression, and poor outcomes. Dermatologists unfortunately confront more difficulties in diagnosing skin illnesses due to their complexity, which includes a wide range of symptoms and subjective interpretation. In order to avoid needless interventions, guarantee proper treatment, and conserve healthcare resources, it makes it more difficult to accurately identify crucial qualities. We use intelligent methodologies, namely Deep Learning with large datasets to improve diagnostic accuracy, to solve misdiagnosis in dermatology. Explainable AI (XAI) approaches specifically designed for skin disease diagnosis are used to train the AI models on a variety of skin disease data sets, enabling them to offer trustworthy diagnostic help. In order to help physicians, comprehend AI-driven diagnoses and foster confidence and collaboration with AI diagnostic tools, we provide them transparency and interpretability. ResNet50V2, VGG16, InceptionV3, and InceptionResNetV2 are the four pre-trained models that have been used. Due to the uncertainty of these models, this work also attempts to use Explainable Artificial Intelligence, which is based on Local Interpretable Model-Agnostic Explanation (LIME), to explain the predictions of these models.