Machine learning refers to the process by which computers may be taught to do certain tasks using a set of algorithms and statistical models. Many different fields and industries may benefit from machine learning, including those dealing with medical diagnostics, audio identification, traffic prediction, statistical arbitrage, and many more. The traffic environment includes anything that might affect vehicle traffic, such as traffic lights, accidents, rallies, or even road maintenance. If the driver or passenger has ideas that are close to all of the above and the many everyday events that could affect traffic, they may make an educated decision. This will also be useful for cars in the future. The quantity of traffic data generated has increased dramatically over the last several decades, and big data approaches have been developed with a focus on transportation. Present traffic glide forecasting algorithms are insufficient for use in real-world, multinational scenarios since they only consider a subset of feasible visitor prediction models. Identifying the traffic flow is a time-consuming process because of the enormous quantity of data accessible for the transportation system. To simplify the analysis, we intended to use deep learning, genetic programming, soft computing, and machine learning to sift through the massive amounts of data collected by the transportation system. Applying Image Processing methods to the problem of traffic sign identification considerably facilitates the proper training of autonomous vehicles. In recent years,