Depression has emerged as a major global health challenge, impacting more than 280 million individuals andcreatingseveresocial, economic, and psychological consequences. Existing digital assessment tools are often cloud-dependent, fragmented, andlimitedby high infrastructure costs, privacy concerns, and lack of integrated crisis management features. To overcome these challenges,theproposed research presents an integrated, offline-capable depression risk identification and crisis intervention systembuilt usingalightweight NLP-based framework. The system performs real-time text analysis through a clinically validated emotionlexicon, detectspotential depressive tendencies, and triggers immediate crisis-response protocols when high-risk indicators appear. It alsoprovidessimulated multimodal analysis—including text, voice, facial, and physiological cues—to enhance educational and researchapplicationswithout compromising data privacy.Experimental validation demonstrates 95 % accuracy in depression detection, 98%sensitivityforcrisis identification, and 91 % correlation with clinical standards, while maintaining an average response time of 1.4seconds.Theplatform operates entirely offline, ensuring full data confidentiality and suitability for academic institutions, rural healthcarefacilities,and research environments. By integrating assessment, intervention, and reporting within a single privacy-preservingframework,theproject bridges the gap between research innovation and practical deployment, offering a scalable, ethical, and accessiblesolutionforglobal mental-health support. Keywords: Depression Detection; Crisis Intervention; Natural Language Processing (NLP); Of line Mental HealthAssessment;Multimodal Analysis; Privacy-Preserving AI; Academic Deployment