Latest research in Blockchain Technology with Federated Learning focuses on integrating decentralized ledger systems with distributed machine learning to enhance data privacy, security, and trustworthiness in collaborative environments. Current studies explore novel architectures and frameworks that combine blockchain consensus mechanisms with federated learning aggregation processes, enabling verifiable model training, ownership authentication, and incentive distribution. Researchers are addressing challenges related to scalability, efficiency, and reliability through methods such as committee-based consensus, asynchronous updates, and trust penalization. Applications span across healthcare, IoT, and industrial edge computing, where blockchain-enabled federated learning ensures secure data sharing, privacy preservation, and efficient collaborative analytics, while also incorporating intelligent aggregation strategies and hybrid privacy mechanisms to optimize model performance in real-world scenarios.