Rapid advances in artificial intelligence (AI), data analytics, cloud platforms, and digital devices have changed both learning environments and health systems. Educational institutions now widely use learning analytics, intelligent tutoring systems, biometric proctoring, and algorithmic assessment. Similarly, health systems apply AI-assisted diagnostics, electronic health records (EHRs), predictive modeling, telemedicine, and federated data infrastructures. While these technologies offer benefits like efficiency, personalization, and improved outcomes, they also introduce complex ethical and privacy issues. These concerns relate to surveillance, autonomy, algorithmic bias, data governance, informed consent, and institutional transparency. This paper examines ethical and privacy considerations across technology-driven learning and healthcare systems, using insights from recent peer-reviewed studies. It argues that despite clear differences between education and healthcare, both face similar risks from increasing data use, opaque AI models, weak regulatory protections, and unequal power dynamics between users and institutions. The paper suggests a unified ethical framework that acknowledges shared principles for fair, safe, and trustworthy data-driven systems.