AI-Driven Network Routing Optimization: An Intelligent Routing System Combining Machine Learning to Predict Link Failures

This article introduces the open-source project AI-Based-Network-Route-Optimizer, which combines a random forest classifier (machine learning technology) with an improved Dijkstra algorithm to implement an intelligent routing system that predicts link failure probabilities and actively avoids high-risk paths. The original author is ParthrChandurkar, the source platform is GitHub, and the release date is June 12, 2026. The core goal is to shift network management from passive response to predictive optimization, improving network availability and performance.

Traditional network routing relies on static configurations and passive response mechanisms, recalculating paths only after a failure occurs, leading to service interruptions, increased latency, and degraded user experience. As network scales expand and traffic patterns become more complex, passive methods can no longer meet high availability requirements. The core challenge is how to identify high-risk links in advance and adjust traffic, requiring an intelligent system that can learn historical patterns, monitor in real time, and predict future behavior.

Random Forest Prediction Model

The system uses a random forest classifier as the prediction engine, inputting real-time telemetry data (latency, packet loss rate, bandwidth utilization) and outputting estimates of future link failure probabilities.

Improved Dijkstra Algorithm

The link weight is changed to a dynamic failure risk score: when the predicted probability exceeds a threshold, the weight increases, and the algorithm naturally avoids high-risk paths to achieve predictive traffic scheduling. This combination does not require large-scale modification of existing facilities; reliability can be improved at the software level.

Applicable to multiple scenarios: data centers predict server link stability and switch critical traffic; wide-area networks help enterprises intelligently select exit points; CDNs actively manage edge node connections; IoT and edge computing enable refined traffic management. This project represents the shift of network management from "reactive" to "predictive" and is expected to become a standard component of next-generation network infrastructure in SDN/NFV environments.

Insights: Combining classic algorithms with ML is more practical (progressive improvements reduce risks); making full use of telemetry data is key to intelligence; the concept of predictive maintenance is extended to the network domain. Outlook: In the future, it can be applied to low-latency scenarios (autonomous driving, industrial control); combining with reinforcement learning to achieve autonomous network management.

AI-Based-Network-Route-Optimizer demonstrates the innovative potential of AI in traditional network engineering. By combining ML prediction capabilities with the reliability of classic algorithms, it provides a reference implementation for next-generation intelligent and reliable networks, which is worthy of in-depth research and practice by network engineers and researchers.