This paper explores the integration of Blockchain technology with Federated Learning (FL) to enhance privacy and security in distributed systems. FL allows multiple participants to collaboratively train a global model without sharing their local data, while Blockchain provides a decentralized, secure framework for data storage and transactions. The paper reviews existing research on privacy-preserving FL with Blockchain, evaluates current architectures, and identifies primary attacks and countermeasures. It also discusses practical applications in healthcare, Industry 5.0, and the Internet of Vehicles. The study highlights the challenges and future directions of Blockchain-enabled FL, emphasizing the need for robust privacy solutions. The paper compares its findings with previous surveys, identifying gaps and offering a comprehensive overview of the field. It outlines the methodology, including search strategies and selection criteria, and provides a detailed analysis of FL and Blockchain concepts. The paper concludes that Blockchain-enabled FL offers significant advantages in privacy, security, and scalability, but further research is needed to address challenges such as data heterogeneity, communication costs, and incentive mechanisms.This paper explores the integration of Blockchain technology with Federated Learning (FL) to enhance privacy and security in distributed systems. FL allows multiple participants to collaboratively train a global model without sharing their local data, while Blockchain provides a decentralized, secure framework for data storage and transactions. The paper reviews existing research on privacy-preserving FL with Blockchain, evaluates current architectures, and identifies primary attacks and countermeasures. It also discusses practical applications in healthcare, Industry 5.0, and the Internet of Vehicles. The study highlights the challenges and future directions of Blockchain-enabled FL, emphasizing the need for robust privacy solutions. The paper compares its findings with previous surveys, identifying gaps and offering a comprehensive overview of the field. It outlines the methodology, including search strategies and selection criteria, and provides a detailed analysis of FL and Blockchain concepts. The paper concludes that Blockchain-enabled FL offers significant advantages in privacy, security, and scalability, but further research is needed to address challenges such as data heterogeneity, communication costs, and incentive mechanisms.