<p>This paper presents a Systematic Literature Review (SLR) on data privacy and security techniques in smart cities, conducted using the PRISMA methodology. The review highlights significant advancements and the challenges associated with ensuring robust data protection due to the growing interconnectivity in urban infrastructures. The study identifies key privacy-preserving techniques that have been widely explored, including IoT security, cyber-physical system protection, Blockchain, and AI-based solutions. While these technologies have demonstrated effectiveness in safeguarding sensitive data, the paper highlights several limitations in their application. Issues such as scalability, interoperability, regulatory compliance, and the complexity of real-time threat detection remain persistent challenges. Additionally, while encryption and access control measures are widely employed, they often fail to provide the flexibility required for dynamic and decentralized systems. In response to these gaps, this paper proposes solutions that include the integration of federated learning for decentralized data processing, AI-enhanced security frameworks for real-time threat detection, and blockchain for immutable data sharing. These advanced techniques aim to overcome the limitations of current systems and pave the way for more secure and efficient data privacy solutions in smart cities. Future research is recommended to focus on optimizing these technologies for broader implementation and addressing scalability issues in large-scale smart city environments.</p>