Accident detection in real-time is critical to ensuring prompt medical assistance and minimizing casualties. Accidents remain a leading cause of death in India. More than 80% of the victims die due to late aid received by them instead of the accident itself. In case of an accident, the victims are not given any early medical attention, especially on highways with high volumes of fast-moving traffic. This paper presents a new approach to accident identification from CCTV surveillance using a hybrid vision transformation framework combined with an automated alert system for notifying the nearest hospital. A system is proposed that would use a hybrid vision transformer for processing real-time footage from CCTV cameras that detect accidents. This proposed model has been suggested using vision transformers with CNNs to enhance efficiency and accuracy in the detection of accidents. The prevalent method for image classification is using CNNs, which are much faster and more accurate than all other techniques. It saves time in medical response by improving the process of accident detection via advanced image processing techniques. The implementation of such a system could make a fundamental difference in road safety and save lives. The system is designed to operate efficiently in diverse environments, such as highways, urban roads, and parking areas, demonstrating high accuracy and scalability.