 This paper proposes a novel artificial intelligence, AI, based traffic incident detection and alert system. It uses three different models, vehicle detection and tracking, traffic accident and severity classification, and fire detection, to detect and alert authorities about any potential incidents. The vehicle detection and tracking model uses the YOLOV-5 object detector and deep sort tracker to identify and track vehicles. The traffic accident and severity classification model uses the YOLOV-5 algorithm to accurately detect and classify an accident severity level. Lastly, the fire detection model uses the ResNet-152 algorithm to detect the ignition of a fire following an accident. All three models were tested using a data set collected from a highway in Saudi Arabia. The results showed that the vehicle detection and tracking model had a mean average precision, map, of 99.2%, the traffic accident and severity classification model had a map of 83.3%, and the fire detection model had an accuracy rate of 98.9%. Additionally, the authors used an innovative parallel computing technique to reduce the overall complexity and inference time of the AI-based system. This article was authored by Mohamed Imran Bashir Ahmed, Rims Aigdoud, Mohamed Saleh Ahmed, and others.