AI-Based Network Traffic Prediction Using Deep Learning for Intelligent Network Management

Abstract The increasing dependence on digital communication, cloud platforms, Internet of Things (IoT) devices, video streaming, and online business applications has led to a significant rise in network traffic. As network usage becomes more dynamic, managing bandwidth and maintaining reliable performance have become major challenges for organizations. Conventional network monitoring systems mainly report the current state of the network and are often unable to anticipate future traffic conditions. Consequently, administrators are forced to respond only after congestion or performance degradation has already occurred. This research proposes a deep learning-based framework for forecasting network traffic by learning patterns from previously recorded traffic data. The framework begins with data collection and preprocessing, where missing values are handled, irrelevant records are removed, and numerical values are normalized. The processed data are then transformed into sequential inputs suitable for deep learning algorithms. A Long Short-Term Memory (LSTM) neural network is employed because of its capability to capture temporal relationships and recognize long-range dependencies in time-series data. The predicted traffic values enable network administrators to anticipate fluctuations in bandwidth demand and implement preventive measures before service quality is affected. The proposed approach supports intelligent decision-making for bandwidth management, congestion mitigation, capacity planning, and routing optimization. It can be applied across enterprise networks, educational institutions, cloud computing environments, and Internet service provider infrastructures. The study demonstrates that deep learning offers a reliable and scalable solution for predictive network management, contributing to improved operational efficiency and enhanced Quality of Service (QoS).