Rib fractures account for 39% to 50% of chest trauma cases, with X-ray imaging being the primary diagnostic tool in clinical practice. However, diagnosing rib fractures remains challenging due to multiple factors, including poor patient positioning caused by pain, overlapping rib structures that obscure fractures, and suboptimal X-ray exposure settings that reduce contrast or introduce noise. Studies indicate that X-ray sensitivity for detecting rib fractures ranges from only 12% to 40.7%, with up to 50% of fractures remaining undetected, whereas computed tomography (CT) achieves a detection rate of 39% to 66%. Although CT improves fracture detection, its high cost and radiation exposure limit its routine use in clinical settings. This study collected X-ray images from patients with CT-confirmed rib fractures and trained four convolutional neural networks (CNNs): AlexNet, VGG16, GoogLeNet, and MobileNetV2 to enhance AI-based fracture detection. The optimized CNN models effectively identified suspected rib fractures, achieving a classification accuracy of 0.77 to 0.98. We further applied the YOLOv4 object detection model to precisely locate fractures on X-ray images, with detailed loss function analysis using CIoU for bounding box regression. For comparative purposes, we discuss RetinaNet's Focal Loss (FL = (1- p)^γ CE, with γ=2) as an alternative to YOLOv4's BCE, potentially addressing class imbalance more effectively. Additionally, EfficientDet was explored as a scalable detector to complement YOLOv4, offering multi-scale feature fusion via BiFPN. The final results showed that among 11 X-ray images, the AI model successfully identified the exact fracture locations in 7 cases, demonstrating its reliability and clinical potential. This study integrates deep learning and image recognition techniques, proposing an AI-based solution that balances diagnostic accuracy and clinical feasibility. By leveraging CNNs to enhance X-ray interpretation and YOLOv4 for precise fracture localization, with comparisons to RetinaNet and EfficientDet, this method reduces the burden on physicians, improves diagnostic efficiency, and optimizes medical resource allocation. The findings confirm that this AI model can effectively assist in the initial assessment of rib fractures, providing clinicians with more accurate decisionmaking support and advancing AI-assisted diagnostic tools in healthcare.