The robust database system known as blockchain technology is designed to effectively address privacy and security concerns in various fields by securely storing transactions within interconnected blocks. Federated learning (FL) is a promising approach to enhancing data mining accuracy while maintaining information privacy as complex contracts between users and smart devices become more prevalent. During the training and testing phases, the collection and aggregation of sensitive data, such as health records, industrial safety information, and banking details, necessitate high levels of privacy and security in Internet of Things (IoT) sectors like smart cities and industries. Incorporating blockchain technology into intelligent learning practices is crucial for safeguarding information security and privacy. Data science and artificial intelligence research are increasingly relying on blockchain-based FL mechanisms. This study systematically reviews privacy and security concerns in blockchain-based FL by examining scientific databases. The objective is to present a summary of the issue's current state. Blockchain-based FL has grown significantly over the past five years, addressing issues with cancer datasets, industrial equipment, patient healthcare records, image retrieval, and economic data in IoT applications and smart environments.