Machine learning-based modern traffic enhancement systems are becoming increasingly popular due to their ability to analyze large amounts of traffic data and make real-time decisions. The number of vehicles on the road has dramatically increased every day in recent years, but sadly, the infrastructure of the roads and traffic systems has not kept up with this growth, leading to ineffective traffic management. This imbalance has not only increased the congestion and traffic bottlenecks on the roads but also led to stressful conditions which amplified road accidents. By utilizing cutting-edge technologies using machine learning Algorithms provided an enriched approach as the solution for traffic congestion and road accidents. In this evaluation of the ML-based traffic management approach various classification techniques such as decision tree, Random forest, Artificial neural network are implemented with Traffic dataset and the accuracy of these algorithms is compared with the proposed model. Among them the proposed obtained higher accuracy than the existing one. The solutions, together with the underlying technology, benefits, and limitations are need to be analyzed rigorously and frame an improved machine learning based traffic solution for avoiding congestion and accidents.