Organizations depend heavily on IT infrastructure, which results in a continuous flow of technical support requests generated through helpdesk systems. Handling these requests efficiently becomes difficult when ticket categorization and prioritization rely on manual analysis. Delays in identifying the type and urgency of issues can reduce the overall effectiveness of IT support services.This research presents an AI-driven IT support ticket management system that automatically analyzes and organizes support requests. The proposed system applies natural language processing techniques to process ticket descriptions and uses TF-IDF feature extraction to represent textual information numerically. A Logistic Regression model is trained to classify support tickets into predefined issue categories and predict their priority levels.The system is implemented as a web-based application using the Flask framework with a SQLite database for ticket storage and management. In addition to automated classification, the platform includes role-based dashboards, communication features between users and support teams, SLA monitoring, and an AI-assisted help center chatbot for troubleshooting guidance. Experimental evaluation demonstrates that the proposed approach can significantly improve the efficiency of ticket management by reducing manual analysis and enabling faster issue routing.
Kumar et al. (Wed,) studied this question.