Wearable devices are solution for real-time stress and health monitoring, leveraging, and body temperature. Of current, technologies reveal diverse applications, from simple consumer electronics to sophisticated biomedical systems, designed to enhance mental health management. These devices utilize advanced algorithms to detect stress levels, providing users with actionable feedback to mitigate anxiety and improve overall well-being. Challenges remain, including the need for improved accuracy, user comfort, and data privacy. Future developments in wearable technology promise to integrate more comprehensive health metrics, paving the way for personalized health interventions and enhancing the efficacy of stress management strategies in everyday life. The increasing prevalence of stress-related health issues has driven the development of wearable devices that monitor physiological indicators for real-time stress detection and health management. This comprehensive review explores various wearable technologies, including smartwatches, fitness trackers, and specialized biomedical sensors, which utilize metrics such as heart rate variability, skin temperature, and electrodermal activity to assess stress levels. Recent advancements have led to the integration of sophisticated algorithms that analyze these signals, offering users actionable insights to manage their stress effectively. Despite the promising capabilities of these devices, challenges such as accuracy, comfort, and data security persist. Moreover, user engagement and adherence to feedback remain critical for the success of these systems. Future research should focus on enhancing the reliability of measurements, expanding the range of physiological parameters monitored, and ensuring seamless integration with mental health applications. The ongoing evolution of wearable technology holds significant potential for personalized health interventions, ultimately contributing to improved mental well-being and resilience in an increasingly stressful world. Wearable devices for stress and health monitoring have emerged as transformative tools in personal healthcare, leveraging advanced technologies to provide real-time insights into physiological responses associated with stress. These devices utilize various sensors to continuously capture critical physiological signals such as heart rate variability, electrodermal activity, skin temperature, and respiration, enabling users to monitor their stress levels and overall health more effectively. The integration of sophisticated algorithms and machine learning techniques enhances the accuracy of stress detection, allowing for personalized feedback and interventions tailored to individual physiological profiles. Recent studies highlight the effectiveness of these wearables in correlating physiological changes with self-reported stress, validating their utility in real-world applications. User satisfaction is notably high, with many individuals reporting an increased sense of control over their stress management through the continuous monitoring and actionable insights provided by these technologies. Despite the advancements, challenges such as ensuring data privacy, enhancing sensor accuracy, and improving user comfort remain critical areas of focus for future research and development. The ongoing evolution of these devices points toward their broader application in mental health management, fostering a proactive approach to healthcare that encourages individuals to engage in preventive measures. By empowering users with real-time data and personalized recommendations, wearable technologies are poised to revolutionize how individuals interact with their health, ultimately contributing to improved mental well-being and quality of life. As research in this field progresses, it is anticipated that these innovations will not only enhance the efficacy of stress monitoring but also integrate seamlessly into everyday life, promoting healthier behaviors and a deeper understanding of the intricate relationship between stress and overall health.