Natural Language Processing (NLP) is transforming mental health care by enabling early detection, diagnosis, and intervention for conditions such as depression, PTSD, and suicidal ideation. Advanced AI models like BioBERT and Med-BERT help analyze speech patterns, cognitive behaviors, and psychological markers to support medical practitioners in providing more effective and timely care. By leveraging deep learning and AI-driven assessments, NLP can enhance behavioral monitoring, improve treatment personalization, and optimize mental health interventions. As global mental health challenges continue to rise, integrating NLP into healthcare systems presents a crucial step toward more accessible, data-driven, and proactive mental health support. Demystifying the Role of Natural Language Processing (NLP) in Mental Health explores the latest advancements in NLP for mental health care, highlighting its role in diagnosing and treating conditions like depression, PTSD, and suicidal ideation. It examines AI-driven models, speech analysis, and behavioral monitoring techniques that enhance early intervention and personalized treatment strategies. Covering topics such as suicidal identification, emotional recognition, and patient response, this book is an excellent resource for mental health practitioners, medical practitioners, social workers, computer scientists, professionals, researchers, scholars, academicians, and more.