LoadMind : AI-Powered Load Balancer and Traffic Management System for High-Traffic Portals

Modern digital platforms frequently struggle to maintain service availability during sudden spikes in user traffic. Traditional load-balancing mechanisms depend on static routing rules or fixed server thresholds, which are often insufficient for large-scale, unpredictable workloads. As a result, users experience downtime, slow responses, or complete service outages. This paper presents an AI-Powered Load Balancer and Traffic Management System, designed to intelligently route requests across distributed servers by learning from real-time performance patterns. The system combines machine learning, predictive analytics, and adaptive traffic control to forecast incoming load, automatically redistribute requests, and ensure stable performance under heavy demand. By integrating AI-based decision models with cloud-native architecture, the proposed solution aims to enhance reliability, reduce latency, and prevent system failures for high-traffic web portals.