Federated Learning (FL) has emerged as a revolutionary approach to machine learning, offering a significant privacy-preserving potential in data analysis in the smart healthcare sector. This systematic review focuses on comprehensively examining the advancements and related difficulties in the application of FL in privacy-preserving smart healthcare. Beginning with an overview of smart healthcare and the importance of privacy in managing sensitive health data, this review introduces FL and compares it with traditional machine learning. The methodology outlines a systematic approach including search strategies, criteria for inclusion and exclusion, data extraction, and quality assessment. FL Applications in smart healthcare, such as electronic health records, medical imaging, predictive modeling, personalized medicine, and telemedicine, have been explored. Various privacy-preserving techniques, including differential privacy, homomorphic encryption, secure multi-party computation, and data anonymization, have been discussed. This review emphasizes the advantages of Federated Learning in healthcare, emphasizing enhanced privacy of data, improved institutional collaboration, scalability, efficiency, and improved patient outcomes. It also addresses challenges, such as technical issues, privacy and security concerns, regulatory and ethical considerations, and the need for interoperability and standardization. Current trends and future directions were examined, pointing to emerging technologies, Artificial Intelligence integration, and future research opportunities. The review concludes by summarizing key findings, implications for practice and policy, and recommendations for future research, providing a comprehensive understanding of FL in privacy-preserving smart healthcare and laying a foundation for further exploration in this critical field.