Skin diseases cause serious challenges in identification and treatment because of their subjective assessment and the lack of reliability of present-day diagnostic tests. This study investigates how to improve the precision and effectiveness of skin disease classification through the implementation of deep learning and machine learning algorithms. This is why better diagnosis tools are required. For this reason, the authors of this study have explored the use of deep learning and machine learning algorithms to enhance accuracy in the diagnosis of skin diseases. The analysis evaluates several models, including custom CNN, ResNet101V2, VGG16, and InceptionResNetV2, alongside machine learning techniques like decision trees, logistic regression, and random forests with YOLOv3 applied for image segmentation. The findings indicate that VGG16 demonstrates a maximum of 98.48% classification accuracy, while decision trees achieve a 99.7% accuracy rate. This paper emphasizes the breakthrough potential of AI in dermatology, calling for further research and performance evaluation and building dependable, instantaneous diagnostic tools for skin diseases.