This study provides the privacy concerns of AI predictive algorithms for Ehealth systems. A significant disadvantage is that these algorithms can infer delicate private health data of people, particularly high-profile figures, from big datasets. This might be infringing privacy and results in discrimination or safety threats. The paper additionally analyzes the danger of AI prediction algorithms escalating wider privacy violation risks for patients and providers like accidental disclosure of personal details or unauthorized use of system vulnerabilities for information theft via AI models. The mixed-method methodology encompasses evaluation of AI algorithm abilities, privacy breach case studies, expert interviews, healthcare provider surveys, and eHealth method penetration tests. The results plot vulnerabilities; risk levels; and technical, cultural, and regulatory variables related to these privacy risks. To lessen those risks, a framework is suggested that has specialized safeguards including AI auditing and differing privacy, governance (data security policies and ethical AI guidelines), organizational (devoted privacy roles and staff training) along with ethical considerations and balance innovation with privacy protection. Lastly, the study suggests multi-stakeholder, strategic and collaborative interaction among healthcare, policymakers, AI designers, and patient advocates to mitigate AI-driven privacy issues in eHealth systems through serious scrutiny and suggestions guided by this vision.