The Social Internet of Things (SIoT) integrates social networking concepts into the Internet of Things (IoT), enabling seamless interaction, data exchange, and intelligent decision-making among connected devices. Machine Learning (ML) plays a pivotal role in enhancing SIoT’s functionality by enabling predictive analytics, personalization, and automation. However, the sensitive nature of SIoT data, coupled with the risks of unauthorized access, adversarial threats, and regulatory constraints, necessitates the adoption of Privacy-Preserving Machine Learning (PPML) techniques. This chapter explores key privacy issues in SIoT, including data sensitivity, sharing risks, and adversarial threat models, alongside ethical and legal considerations such as GDPR and HIPAA compliance. Foundational aspects of PPML are discussed, covering its objectives, critical frameworks, and the trade-offs between efficiency, privacy, and accuracy. Prominent privacy-preserving methods are examined, including federated learning, homomorphic encryption, secure multi-party computation, differential privacy, blockchain mechanisms, and hybrid approaches. The chapter also outlines the privacy-preserving model lifecycle in SIoT, from secure data gathering and anonymization to privacy-aware training, deployment, and maintenance. Real-world applications and case studies in smart homes, intelligent transportation, healthcare, wearables, and industrial IoT are presented to illustrate practical implementations. Performance evaluation metrics, scalability challenges, and comparative analyses of privacy techniques are provided to assess their effectiveness. Finally, the chapter addresses ongoing challenges such as balancing privacy with system performance, defending against adversarial attacks, ensuring interoperability, and adapting to emerging trends in privacy-preserving AI for SIoT. The insights aim to guide researchers, developers, and policymakers in building secure, efficient, and trustworthy SIoT ecosystems.