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Cancer with its early and accurate diagnosis tremendously improves both the success and survival of the cancer patients. The paper presents an AI-assisted medical imaging-based diagnostic tool that identifies different cancer types. The framework combines the use of Convolutional Neural network (CNN, InceptionV3, and Vision transformer (ViT) in carrying out multi-class classification of cancers such as: brain, breast, oral, lung, cervical, lymphoma, and kidney cancers. A Streamlit-based web-based interface is used to upload images in real-time, along with making the predictability. The system shows the good prospects related to the classification accuracy, scale, and usability and can be used in the healthcare domain to support clinical decision-making.