In healthcare, IoT-based applications are growing day by day to perform predictions regarding chronic diseases using machine learning. There is a widespread consensus that melanoma, or cancer of the skin, is one of the worst illnesses in the world. A precise classification of skin lesions in their early stages might likely assist in the process of therapeutic decision-making, therefore improving the probability of a cure before cancer develops. The likelihood of developing cancer of the skin is highest in those parts of the body that are often exposed to the damaging effects of direct sunlight. In men, these body parts include the head, face, lips, and ears; in females, the chest, arms, and hands; and in both sexes, the legs. On the other hand, it is also possible for it to develop on sections of your body that are seldom exposed to air and light, such as your hands, feet, and other spots on your body. Deep learning has been considered a subset of machine learning that is frequently used to develop detection and classification mechanisms. In the current study, the deep learning methodology is being considered as a potential method for detecting skin cancer in a shorter amount of time and with more precision. Compression operations have been performed to increase performance, and hybrid deep learning models have been used to improve accuracy measures such as recall, precision, and f1-score. In the IoT environment, individuals can benefit from continuous and real-time skin health monitoring. As ML algorithms continue to evolve and gain access to extensive datasets, their role in skin cancer detection and recognition within IoT holds immense promise for enhancing healthcare and prevention strategies.