Coronary artery disease (CAD) is a prevalent cardiovascular condition and a leading cause of mortality. An accurate and timely diagnosis of CAD is crucial for treatment. This study aims to detect stenosis in real-time and automatically during angiographic imaging for CAD diagnosis, using the YOLOv9c model. A dataset comprising 8325 grayscale images was utilized, sourced from 100 patients diagnosed with one-vessel CAD. To enhance sensitivity and accuracy during the training, testing, and validation phases of stenosis detection, fine-tuning and augmentations were applied. The Python API, utilizing YOLO and Ultralytics libraries, was employed for these processes. The analysis revealed that the YOLOv9c model achieved remarkably high performance in both processing speed and detection accuracy, with an F1-score of 0.99 and mAP@50 of 0.99. The inference time was reduced to 18 ms, fine-tuning time to 3.5 h, and training time to 11 h. When the same dataset was tested using another significant diagnostic algorithm, SSD MobileNet V1, the YOLOv9c model outperformed it by achieving 1.36 × better F1-score and 1.42 × better mAP@50. These results indicate that the developed YOLOv9c algorithm can provide highly accurate and real-time results for stenosis detection.