This paper explores the integration of Blockchain technology with Federated Learning (FL) to enhance privacy and security in collaborative machine learning. FL allows multiple devices or servers to train a global model without sharing their local data, but this distributed nature raises concerns about data privacy and security. By combining FL with Blockchain, the central authority is replaced by a decentralized network, ensuring that only aggregated model updates are shared, thus preserving participant privacy. The paper reviews existing research on Blockchain-enabled FL, evaluating various architectures and their integration with FL. It identifies primary privacy threats, such as background knowledge attacks, collusion attacks, and inference attacks, and discusses countermeasures like differential privacy, homomorphic encryption, and secure multiparty computation. The paper also highlights practical applications of Blockchain-enabled FL in sectors like healthcare, Industry 5.0, and the Internet of Vehicles. Key contributions of the paper include: - A comprehensive overview of FL and Blockchain technologies. - An analysis of existing architectures for Blockchain-enabled FL. - Identification of primary privacy threats and countermeasures. - Examination of practical applications and future research directions. The study aims to provide a valuable resource for both academic researchers and industry practitioners, helping them understand the current landscape and future directions in the field of Blockchain-enabled Federated Learning.This paper explores the integration of Blockchain technology with Federated Learning (FL) to enhance privacy and security in collaborative machine learning. FL allows multiple devices or servers to train a global model without sharing their local data, but this distributed nature raises concerns about data privacy and security. By combining FL with Blockchain, the central authority is replaced by a decentralized network, ensuring that only aggregated model updates are shared, thus preserving participant privacy. The paper reviews existing research on Blockchain-enabled FL, evaluating various architectures and their integration with FL. It identifies primary privacy threats, such as background knowledge attacks, collusion attacks, and inference attacks, and discusses countermeasures like differential privacy, homomorphic encryption, and secure multiparty computation. The paper also highlights practical applications of Blockchain-enabled FL in sectors like healthcare, Industry 5.0, and the Internet of Vehicles. Key contributions of the paper include: - A comprehensive overview of FL and Blockchain technologies. - An analysis of existing architectures for Blockchain-enabled FL. - Identification of primary privacy threats and countermeasures. - Examination of practical applications and future research directions. The study aims to provide a valuable resource for both academic researchers and industry practitioners, helping them understand the current landscape and future directions in the field of Blockchain-enabled Federated Learning.