Healthcare organizations increasingly rely on machine learning (ML) techniques to support disease diagnosis, clinicaldecision-making, patient risk prediction, and personalized treatment planning. The rapid digitization of Electronic HealthRecords(EHRs), medical imaging repositories, laboratory reports, and genomic databases has generated vast amounts of healthcaredatasuitablefor intelligent analytics. However, healthcare datasets contain highly sensitive personal information, making privacy preservationoneofthe most critical challenges in medical machine learning. Unauthorized disclosure of patient information may violate legal regulations,reduce public trust, and discourage data sharing among healthcare institutions. Consequently, developing privacy-preservingmachinelearning (PPML) techniques capable of maintaining high classification accuracy while protecting patient confidentialityhasbecomeanimportant research objective. This study presents a comprehensive investigation of privacy-preserving machine learningtechniquesforhealthcare data classification. Although modern PPML techniques such as federated learning and differential privacy becameprominentafter 2015, the period 2008–2015 established the fundamental concepts of privacy-preserving data mining, secure multipartycomputation,homomorphic encryption, anonymization, cryptographic protocols, and machine learning-based healthcare classification. Thesefoundational technologies provide the theoretical basis for contemporary privacy-preserving artificial intelligence systems. Theproposedframework integrates secure data preprocessing, privacy-preserving feature selection, intelligent classification algorithms, encryptedmodel training, and secure healthcare decision support into a unified architecture. Keywords: Privacy-Preserving Machine Learning, Healthcare Data Classification, Electronic Health Records, HealthcareAnalytics,Machine Learning.