The increasing prevalence of firearms poses significant challenges to public safety, particularly in high-risk environments such as schools, airports, and transportation hubs. This study explores the implementation and performance of YOLOv11, the latest advancement in the YOLO series, for handgun detection in static images, video streams, and real-time CCTV monitoring. By leveraging its transformer-based architecture and adaptive scene understanding, YOLOv11 achieves exceptional accuracy, low latency, and minimal false positives across diverse scenarios. The results demonstrate YOLOv11’s superiority over previous iterations in precision, speed, and robustness, making it a reliable solution for real-time threat detection. This research underscores the potential of integrating YOLOv11 into modern surveillance systems to enhance public safety and crime prevention efforts.