Real-time monitoring of traffic is crucial for ensuring continuity in network environments because it introduces minimal amounts of downtime, identifies anomalies and manages congestion. Accurate analysis of live network data determines irregularities or just before predictability of congestion hotspots in networks, and that will make proactive decisions possible for network managers. This paper focuses on the machine learning-driven approach to real-time traffic monitoring, where more advanced models like autoencoders, Isolation Forests and LSTM networks are considered for better anomaly detection and congestion prediction. For each model, it tests how effectively they find anomalies in the network traffic, discussing how one complements another in capturing better anomalous patterns. This incorporates autoencoders and LSTM networks, as both can model highly complex data structures, while employing the Isolation Forest because, in practice, it is often very efficient when searching for anomalies in high-dimensional datasets. Using all these types of models enables it to detect anomalies with considerable scope. One of those is predictive analytics which would notify the network managers before the onset of potential traffic congestion. This enables the network managers to preventively take action to maximize the usage of the available resources. Another one is a highly understandable dashboard of visual insights that was added to the processes along with live updates on network status and level of congestion. It concluded that several models of machine learning should be used to address the complexities brought about by monitoring network traffic. The system would contribute to the development of resilient traffic management strategies that would be able to meet the challenges of a modern, dynamic network environment with a scalable, stable and adaptable solution.