Brain tumor detection is still an important challenge in medical imaging. It has become one of the many keys to getting the best results for patients. The latest model of YOLO and its application for brain tumor detection from MRI scans are deliberated upon in this study. For that, our methodology is based on training YOLOv10 on a comprehensive dataset of 1003 MRI scans, including different kinds of tumors of varying sizes. Results show that YOLOv10 achieves a high detection accuracy and drastically eliminates false positives. Furthermore, YOLOv10 is found highly effective even for small-sized, low-contrast tumors, a challenge that often occurs in automated detection systems. These results indicate that YOLOv10 is a vital advancement in the brain tumor detectors, which has the potential to culminate in improved diagnostic accuracy and efficiency in the operating room. Our study demonstrates the fast progress in “deep-learning” based medical image analysis and its promising implications for improving patient care in neuro-oncology.