The purpose of this study is to create a tool for anticipating traffic flow information that is both accurate and timely. The term “traffic environment” refers to any factors that may impact how quickly traffic lights, collisions, demonstrations, and even road works that can cause a backup are all factors that affect how quickly traffic travels down the road. A driver or passenger can make an informed choice if they have prior information that is very nearly true about all the aforementioned aspects as well as many more actual situations that can affect traffic.Also, it aids in the future of autonomous vehicles. Traffic data have been growing dramatically in the recent decades, and we are moving towards big data concepts for transportation. The current approaches for predicting traffic flow use some traffic prediction models, however they are still inadequate to handle practical situations. Due to the enormous amount of data that is available for the transportation system, it is difficult to forecast the traffic flow accurately.We were inspired by this reality to use traffic data and models to solve the challenge of traffic forecasting..It this work, we intended to analyse the big-data for the transportation system with significantly less complexity by utilising machine learning, genetic, soft computing, and deep learning techniques. Additionally, Image Processing algorithms are involved in traffic sign identification, which finally aids for the appropriate training of autonomous vehicles.