The modern network has expanded significantly due to the rapid rise of network usage, making it a big, dynamic, and complicated system. The management of modern network traffic has now become a major challenge as a result of large-scale network-based applications. Consequently, the smart traffic analysis-based monitoring of networks has become an urgent need. Network traffic classification is an important approach used in network management, as well as in network security. Traditional methods often require ongoing maintenance, struggle with dynamic ports, and lack the granularity needed for precise classification. They can also be resource-intensive and have scalability issues for large networks. To overcome these limitations, many organizations are turning to more advanced techniques like machine learning and behavior analysis for better network traffic classification and security. Accurate classification is crucial for network traffic due to its multifaceted importance. It serves as the foundation of network security by enabling the rapid detection of security threats and illegal activity, which is critical for protecting against cyberattacks. This study suggests a smart, intelligent system based on a BAT artificial network for network traffic classification. The proposed system makes use of a publicly available NIMS dataset. Furthermore, we have applied some preprocessing and feature selection techniques before feeding the data into the classifier. The experimental outcomes reveal that the proposed approach achieved high accuracy and low computation time, and performed better than the previous approaches used for network traffic classification.