This paper proposes an improved wearable biosensor system, empowered by AI, for real-time monitoring and prediction of the level of stress based on physiological parameters. The proposed system incorporates multiple biosensors that track key biomarkers related to heart rate, galvanic skin response, body temperature, blood oxygen levels, respiration rate, and even inferred levels of cortisol. This system makes use of the underline machine learning algorithms in order to identify all kinds of changes related to stress using the data that is being collected, thus providing health insights on time. Assessments of each system performance were based on accuracy, sensitivity, and specificity, which were of 94%, 92.5%, and 95.2%, respectively. They were compared against metrics regarding existing studies, therefore showing superior results in the detection of stress with higher precision. Indeed, the performance is robust where the advanced AI model consolidates most of the physiological markers to elicit better detection of stress. This result really points to the great potential of enhanced wearable biosensors with artificial intelligence for personalized healthcare—to let each and every individual monitor his or her health on a continuous scale with real predictive information at various moments in time on the level of stress and related health conditions.