The detection of violent content in animated cartoons is an essential step toward safeguarding young audiences and promoting responsible media consumption. This study introduces an automated approach to identify violent scenes in cartoons using advanced object detection models. A custom dataset comprising 2,000 frames was curated from various animated sources, focusing on four key classes: Explosion, Blood, Fight and Gunshot. Data augmentation techniques, including rotation, scaling, and color adjustments, expanded the dataset to 3,000 frames, enhancing diversity and model generalization. YOLO versions 8, 9, and 10 were trained and evaluated on this dataset. Among these, YOLOv9 achieved the highest performance with a mean Average Precision (mAP) of 94%, demonstrating superior accuracy and robustness. These findings highlight YOLOv9’s potential as a reliable tool for detecting violent content in animated media, contributing to the development of effective content moderation systems.