Traffic examination has many purposes, for example, assessing the exhibition and security of organization activities and the executives. Thusly, network traffic examination is viewed as crucial for further developing organizations activity and security. This paper examines different AI approaches for traffic examination. Expanded network traffic and the advancement of man-made consciousness require better approaches to identify interruptions, examine malware conduct, and order Web traffic and other security perspectives. AI (ML) shows viable abilities in taking care of organization issues. Traffic characterization using stream estimation empowers administrators to perform fundamental organization the executives. Stream bookkeeping strategies, for example, Net Flow are, be that as it may, considered insufficient for arrangement requiring extra bundle level data, have conduct examination, and specific equipment restricting their common sense reception. This paper intends to conquer these difficulties by proposing two-staged AI order system with Net Flow as information. The singular stream classes are determined per application through - implies and are additionally used to prepare a C5.0 choice tree classifier. Besides, the computational presentation and precision of the proposed system in examination with comparative AI methods lead us to prescribe its augmentation to different applications in accomplishing exceptionally granular continuous traffic order.