The pressing need for secure, private, decentralized frameworks for machine learning in healthcare has been fueled by the increasingly popularization of Federated Learning (FL). In conventional FL, the aggregation is centralized, allowing potential data leakages or model-poisoning attacks against a central point of failure. A possible solution to these aforementioned impediments is Blockchain-Based Federated Learning (BDFL), as such a setup can utilize the immutability, transparency, and distributed consensus of the blockchain to enhance security and achieve better performance. Nevertheless, the existing review articles have not offered a thorough investigation of BDFL consensus algorithms, their specific applications to the healthcare sector, and an iteratively empirical performance evaluation of their efficiency, scalability, and robustness. This paper provides a systematic and empirical review of state-of-the-art BDFL consensus programs in their application to health care; it analyzes these programs’ performances based on consensus efficiency, incentive mechanisms, privacy-preserving capabilities, and computational scalability. Key approaches examined in this study include Proof-of-Contribution (PoC) [2, 3], Byzantine Fault Tolerance (BFT) [5], DAG-based Blockchain FL [4, 13], Multi-center Federated Learning (MCFL) [24], and Proof-of-Accuracy (PoAcc) [20]. The reason for this focus is that these methods best integrate security, efficiency, and fairness in the context of decentralized health data cooperation. The results indicate that MCFL models would optimize institution-wise healthcare cooperation, PoAcc would optimize the accuracy of medical diagnosis, and the DAG-based blockchain would guarantee high throughput scalability for FL. This review sets out an extensive framework for selecting the best models in BDFL, which will encourage developments in AI-nurtured healthcare data analysis, clinical decision support, and secure EHR management. This study’s findings will propel future advancement in federated learning security, quantum-safe consensus mechanisms, and hierarchical blockchain architectures for global health applications.