The human skin, which is the biggest organ and the outermost layer of the body, has seven layers that serve to shield interior organs. Because of its broad role in the integumentary system, maintaining the health of the skin is essential. Skin problems present substantial classification challenges for medical professionals since they include a wide spectrum of diseases, including dermatoses. Consequently, they are depending more and more on machine learning (ML) technologies to help them predict and categorize these diseases. In the field of imaging, convolutional neural networks (CNNs) have demonstrated performance that is comparable to, and in some cases surpasses, human capabilities. Within this research, we propose a novel CNN architecture designed to classify two specific skin diseases: Eczema (symptoms on legs and hands) and Seborrheic Keratoses (symptoms on ears and skin). Additionally, we compare the performance of six ML algorithms to determine the most accurate model. We trained and tested our proposed technique on the Dermnet 2021 DATASET, which consists of 2,332 pictures and is publically available on Kaggle. Our findings show that the suggested CNN model, which achieves an accuracy of 91.1% and an F1-score of 92.3%, surpasses other cutting-edge techniques. With an F1-score of 79.12% and an accuracy of 78.41%, Linear Regression (LR) was the most successful ML model that was examined.