This research presents an innovative brain tumor detection and localization approach using the advanced deep learning model, YOLOv9. The superior performance and processing capabilities of this model has been leveraged in this study to address critical challenges in brain tumor detection and localization using medical imaging. The YOLOv9 model has been meticulously trained on a comprehensive dataset of annotated brain MRI scans, achieving remarkable precision in identifying and localizing tumors of various sizes and types. Through extensive experiments, the model has demonstrated marked improvements over previous YOLO versions and other state-of-the-art methods, particularly in detection speed and localization accuracy. The findings suggest that YOLOv9 can substantially enhance diagnostic workflows, and offer a robust tool for early and accurate tumor detection. This advancement holds promise for improving patient outcomes and streamlining medical image processing, potentially setting a new standard in applying deep learning in healthcare. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.