This paper introduces a lightweight AI-powered framework designed to enhance technical support operations within JIRA-based environments. By integrating Python scripts and machine learning models, the system automates ticket classification based on urgency and predicts potential SLA breaches before they occur. The framework uses historical ticket data to train classification algorithms, enabling proactive routing and escalation through JIRA’s REST API and Automation Rules. In real-world testing, the solution demonstrated a 34% reduction in ticket resolution time and improved SLA adherence by 40%. This approach eliminates the need for expensive plugins or enterprise licenses, making it a scalable and cost-effective automation strategy for small to mid-sized IT support teams.