Traffic has increased significantly over the past few years as a result of an increase in the number of vehicles using the common routes. It has been noted that manually operating signals has grown to be difficult. The difficulties encountered during an accident are the backed-up ambulances during emergencies caused by vehicle congestion and poor weather conditions like fog and haze. Considering this, the study focuses on the outcomes of an intelligent accident detection system using Vehicular Fog Computing (VFC). Automatic identification of crash spots and free flow of ambulances on roadways at peak hours of traffic. VFC has recently gained popularity as a useful tool for assisting vehicles in computing and storing service demands. Using the built-in sensors on a smartphone to monitor vehicular collisions and report them to the closest accessible first responder, as well as providing real-time location monitoring for paramedics and emergency victims would greatly improve the odds of recovery for emergency victims while saving time and money. This computing model guarantees the optimization of traffic congestion and energy consumption in the accident and foggy environment. This method also relies on delivering medical records to the closest hospital before the ambulance arrives, so pre-treatment can begin in the hospital.