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This research explores the development and evaluation of an automated patch management framework aimed at improving vulnerability mitigation in complex IT environments. With the rising frequency and sophistication of cyber threats, traditional manual patching methods have proven to be inefficient, error prone, and unable to scale with modern infrastructure demands. Through a combination of systematic literature review, prototype development, experimental testing, and user feedback, the study demonstrates that automation significantly reduces patch deployment time and system downtime, enhances mitigation success rates, and lowers false positive occurrences. The proposed system integrates machine learning for risk based prioritization, real time vulnerability assessment, and sandbox testing to ensure accurate and secure patch application. Empirical results show an 80 improvement in deployment speed and a 27 increase in mitigation success compared to manual methods. The findings highlight the transformative potential of automated patching in strengthening cybersecurity posture and suggest its strategic implementation as a critical component of modern vulnerability management. Shivaraj Yanamandram Kuppuraju | Chandrashekhar Moharir | Vineet Baniya "Patch Management Automation for Vulnerability Mitigation" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-9 | Issue-3 , June 2025, URL: https://www.ijtsrd.com/papers/ijtsrd79992.pdf Paper URL: https://www.ijtsrd.com/engineering/computer-engineering/79992/patch-management-automation-for-vulnerability-mitigation/shivaraj-yanamandram-kuppuraju