Over recent years, video-surveillance systems have seen extensive adoption, largely driven by security imperatives, with radar-based speed detection being a common feature in traffic monitoring. Despite its prevalence, broader anomaly detection in traffic patterns has not received equivalent focus. This research develops a sophisticated deep learning framework, drawing architectural inspiration from MobileNet, ResNet50, and VGG19, to not only detect and track vehicles but also analyze trajectory data to identify nonstandard behaviors. Specifically, our model detects four distinct anomalies: overspeeding, lingering in no-stopping zones, insufficient spacing between vehicles, and violations of traffic light signals. To support this, we constructed a unique dataset comprising over 60,000 video frames. The YOLOv3 algorithm facilitated initial object recognition, which was complemented by data augmentation techniques to mitigate issues related to class imbalance and the limited availability of annotated datasets in this domain. Our enhanced model achieved an overall accuracy of 95%, with a detailed performance breakdown for each detected anomaly.