Skin diseases present a major threat globally, and the potential integration of AI techniques into skin disease detection is enormous. Skin diseases are among the most neglected types of diseases. One approach is to create a dataset of multiple diseases and analyze them using AI technologies. By creating an AI-based Deep Learning model using ResNet, this research aims to develop a system through which users can easily detect their skin diseases. Individuals with skin diseases, especially in rural areas, often tend to neglect them, so we aim to provide a solution that allows them to analyze their condition from the convenience of their home. One problem acting as an obstacle to our model is noisy and unevenly dimensioned data. We would be employing cutting-edge technologies like TorchVision, sickit-learn, PyTorch, and many more. This research aims to enhance performance and address the shortcomings of previous models by utilizing cutting-edge ResNet technology. Furthermore, researchers and doctors can carefully examine the dataset to aid in the development of better medical technologies and cures. This research intends to assist doctors in the preliminary examination of skin diseases. A remarkable result of our approach using the ISIC dataset is 99.3% accuracy. Due to this high level of accuracy and precise results, our model is reliable and can provide patients, doctors, and researchers with valuable insights.