Cancer poses a significant global health challenge, where early detection is pivotal for effective treatment. Traditional diagnostic methods such as biopsies and imaging are invasive, costly, and time-intensive. Our research focuses on leveraging three widely used deep learning algorithms to detect and classify cancerous images. Specifically, we employ pre-trained convolutional neural network architectures—DenseNet201, MobileNetV3, and VGG16—on a substantial dataset comprising over ten thousand images per cancer type, encompassing Lung and Colon Cancer, Breast Cancer, Cervical Cancer, Brain Cancer, Acute Lymphoblastic Lymphoma, Oral Cancer, Kidney Cancer, and Lymphoma. The novelty of our work lies in utilizing CNN models to analyze diverse cancer images and interpret results. This study contributes to consolidating various cancer detection methods into a unified approach, aiming to create a comprehensive and adaptable screening or diagnostic tool capable of efficiently and accurately identifying multiple cancer types. Rather than relying on separate and specific detection methods for each cancer type, our research strives to develop a holistic solution that streamlines the diagnostic process and enhances screening accessibility.