A brain tumor is one of the worst forms of cancer. Its placement, so close to the main neural actuator in the human brain, has far-reaching repercussions since even a little malfunction there might lead to millions of dollars in lost income. Robotic tumor detection and magnetic resonance (MR) segmentation methods might need some innovation in order to improve their accuracy. The sphere of image processing and diagnostic imaging has been dramatically affected by CNN. As time has progressed, it has become a crucial framework for analyzing medical images and identifying diseases. When it comes to tasks that are considered very complex and need a brain impulse to finish, convolutional neural networks have shown remarkable ability. Thanks to their efficacy, CNN structures have found a place in several medical specialties. One of the most essential ways for early identification of tumor masses that are carcinogenic is CNN technology, which has been rapidly advanced by digital systems. Developing a hybrid deep learning system capable of early cancer detection and identification is the primary goal of this paper.