This research introduces an accurate brain tumor detection, segmentation, and classification system in the medical field deployed on Streamlit to answer the urgent demand for a more precise and effective diagnosis of brain tumors. The existing problems in this field include a small number of available MRI modalities, a classification system that only includes a few types of brain tumors, and the division of segmentation and classification responsibilities within the systems that are currently in place. Our strategy involves Neural Network Transfer Learning and finetuning approach to address these problems. We have accomplished important milestones by utilizing transfer learning on top of the YOLOv8 pretrained model provided by the Ultralytics library that achieved a classification accuracy of over 95% and a segmentation accuracy of approximately 90%.