Depression is a major global health challenge that affects millions of people across demographic and geographical boundaries, with far-reaching consequences for the emotional well-being, economic stability, and social functioning of individuals. Traditional diagnosis, mainly through clinical interviews and self-reporting tools, has several limitations in nature—being episodic, subjective, and limited in access to mental health resources. Recent years have seen the emergence of artificial intelligence, machine learning, and the Internet of Things, which provide groundbreaking opportunities for the continuous and objective monitoring of depressive symptoms. These technologies utilize a wide array of data sources, such as textual, vocal, visual, and physiological markers, to enable more comprehensive diagnostics and timely interventions. This chapter reviews state-of-the-art Artificial Intelligence (AI) and Internet of Things (IoT) implementations in mental healthcare and discusses their potential to revolutionize the detection and monitoring of depression. Critical technological advances include advanced Natural Language Processing (NLP) algorithms for text analysis, new Deep Learning (DL) frameworks for speech pattern and facial expression recognition, and innovative wearable devices for continuous physiological monitoring. While these technological solutions hold promise in delivering personalized, proactive mental health care, there are critical considerations about data security, algorithmic transparency, and equitable access. We also chart the intricate landscape of challenges, ethical implications, and regulatory frameworks critical to successful clinical integration. The convergence of AI and IoT technologies, with addressing in mind the current methodological limitations, holds transformative potential in the delivery of mental health care; it promises greater accessibility, scalability, and precision in the diagnosis and treatment of depression.