The research presented here aims to develop an improved hybrid model for skin cancer detection. By integrating a convolutional neural network (CNN) and a long-term short-term memory (LSTM) network, the model recognizes seven types of skin lesions: melanocytes, nevi, skin cancer. and benign tumors Basal cell keratosis-like lesions, leveraging the spatial feature extraction capability of CNN coupled with chronological learning of actinic carcinoma, the system will increase the diagnostic accuracy. And the performance model is rigorously trained and validated using a comprehensive dataset of skin camera images. It provides promising results in demonstrating early detection. This is critical in improving patient outcomes in the field of dermatology.