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Mental health disorders, affecting nearly one billion people globally, pose a silent yet pervasive threat to well-being, reducing life expectancy and straining families, workplaces, and healthcare systems. Traditional management tools, clinical interviews, questionnaires, and infrequent check-ins fall short, hampered by subjective biases and their inability to capture the nature of conditions. This chapter explores how wearable technologies, powered by advanced sensors, artificial intelligence (AI), and machine learning (ML), are revolutionizing mental health care by enabling continuous, objective monitoring. Focusing on four approaches – physiological, neurotechnological, contactless, and multimodal we analyze their mechanisms, applications, and transformative potential. These innovations promise proactive care, early intervention, and greater accessibility, yet face challenges. By integrating AI and refining device design, wearable technologies could redefine mental health management, empowering field, though their success hinges on overcoming technical and ethical hurdles. 

Stress has been recognized as a pivotal indicator which can lead to severe mental disorders. Persistent exposure to stress will increase the risk for various physical and mental health problems. Early and reliable detection of stress-related status is critical for promoting wellbeing and developing effective interventions. This study attempted multi-type and multi-level stress detection by fusing features extracted from multiple physiological signals including electroencephalography (EEG) and peripheral physiological signals. Eleven healthy individuals participated in validated stress-inducing protocols designed to induce social and mental stress and discriminant multi-level and multi-type stress. A range of machine learning methods were applied and evaluated on physiological signals of various durations. An average accuracy of 98.1% and 97.8% was achieved in identifying stress type and stress level respectively, using 4-s neurophysiological signals. These findings have promising implications for enhancing the precision and practicality of real-time stress monitoring applications.