Rapid breakthroughs in biotechnology, digital health surveillance, and translational medicine have introduced critical normative and regulatory dilemmas across global healthcare ecosystems. This paper explores the ethical, legal, and public health policy dimensions surrounding Ethical Considerations in AI-Driven Predictive Diagnostics through a systematic multi-disciplinary framework. Integrating normative bioethical principles of beneficence, autonomy, non-maleficence, and distributive justice with empirical qualitative surveys from clinical practitioners and bioethics review boards, the study critically analyzes systemic vulnerabilities in contemporary governance protocols. The research demonstrates that existing regulatory instruments frequently fail to address algorithmic bias, informed consent complexities, data sovereignty rights, and equitable cross-border access to therapeutic innovations. To resolve these challenges, the authors propose a robust participatory governance model incorporating continuous algorithmic auditing, stakeholder deliberative forums, and standardized global bioethics oversight guidelines. These insights provide valuable normative and operational recommendations for health ministries, institutional review boards, and biomedical researchers striving to protect human rights while fostering scientific innovation.