Artificial Intelligence (AI) is redefining healthcare through enhanced diagnostics, personalized interventions, and preventive monitoring. Privacy-preserving AI-enabled health applications—leveraging technologies such as federated learning and differential privacy—hold the potential to protect sensitive health data while delivering actionable insights. Yet, adoption among Generation Z (Gen Z) remains inconsistent, hindered by complex and interlinked barriers. This study identifies and models ten critical barriers—Awareness Deficit, Data Misuse Anxiety, AI Credibility Doubt, Surveillance Concern, UX–UI Friction, Digital Health Knowledge Gap, Regulatory Ambiguity, Human Displacement Fear, Perceived Health Irrelevance, and Eco–Ethical Concerns—through a survey of 142 Gen Z respondents in India. Using Interpretive Structural Modeling (ISM) and MICMAC analysis, the research reveals a hierarchical structure were foundational drivers cascade into immediate deterrents, demonstrating that isolated interventions may be insufficient. By integrating Protection Motivation Theory (PMT) and the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2), the study offers a dual lens capturing both resistance (security–privacy dimension) and adoption (utility–experience dimension) dynamics. Findings provide actionable guidance for developers, policymakers, educators, and healthcare providers to address root causes, bridge knowledge gaps, enhance trust, and align solutions with Gen Z’s values, fostering informed and sustained adoption.