Suicide accounts for one of the leading causes of death in India, with over 164,033 deaths reported in 2021. Despite increased awareness, the gap between the need for consistent treatment and actual accessibility remains a challenge due to limited mental health infrastructure, the stigma surrounding mental illness in society, and the lack of real-time detection mechanisms. Traditional suicide risk assessments often miss early signs of distress, which rely heavily on clinical evaluations and self-reporting. Although AI-based monitoring seems promising, currently available models focus only on risk prediction without intervention and treatment, leaving a critical gap in tackling crisis management. In this proposal, we strive to design SAHAY, the first-of-its-kind AI-based, suicide prevention framework that seamlessly couples prediction with prevention and treatment access. Leveraging multimodal data, including the social media text and Electronic Health Records (EHR) and Ecological Momentary Assessments (EMA) such as wearable physiological data, SAHAY aims to assess suicide risk dynamically. Unlike existing models, SAHAY is culturally adaptive, multilingual and seamlessly integrates with India’s TeleMANAS mental health support system, to provide structured AI-human collaboration for long-term care and crisis interventions. It will be an adaptable, scalable, modular, and plug-and-play solution based on the Digital Public Infrastructure principle. Additionally, we intend to incorporate AI-driven geo-spatial crisis mapping to identify suicide hotspots in underserved regions. By combining real-time multimodal risk detection, professional mental health intervention, and geo-spatial outreach, SAHAY represents a scalable, adaptable, and end-to-end suicide prevention system. The design of SAHAY aligns with UN Sustainable Development Goals (SDGs) 3, 4, 5, 10, and 17, promoting inclusive, accessible, and data-driven mental healthcare.