AI-DRIVEN WEB-BASED TRACKING FOR REAL-TIME MONITORING AND MANAGEMENT

Abstract

With the rapid growth of digital platforms and the increasing demand for efficient monitoring solutions, web-based tracking systems have become essential in diverse domains such as logistics, healthcare, workforce management, and security. Traditional web-based tracking solutions, however, often face limitations in scalability, adaptability, and intelligent decision-making. This paper presents an AIdriven web-based tracking system designed to enhance realtime monitoring, predictive analysis, and automated decision support. The system integrates machine learning models to analyze user patterns, detect anomalies, and optimize tracking performance, while leveraging cloudbased infrastructure for scalability and accessibility. Experimental evaluation demonstrates that the proposed AIenhanced framework significantly improves accuracy, reduces response time, and provides actionable insights compared to conventional web-based tracking systems. This research highlights the potential of combining artificial intelligence with web technologies to achieve more efficient, intelligent, and adaptive monitoring solutions