Music plays a critical role in emotional regulation and stress relief; however, individuals often need different types of music tailored to their unique stress levels or surrounding environment. Choosing the right music can be challenging due to the overwhelming number of options and the time-consuming trial-and-error process. To address this, we propose Context-AI Tune (CAT), a system that generates personalized music based on environmental inputs and the user’s self-assessed stress level. A \(2\times 2\) within-subject experiment (N = 26) was conducted with two independent variables: AI (AI, NoAI) and Environment (Busy Hub, Quiet Library). CAT’s effectiveness in reducing stress was evaluated using the Visual Analog Scale for Stress (VAS-S). Results show that CAT is more effective than manually chosen music in reducing stress by adapting to user context.