Preserving privacy in healthcare data is crucial for maintaining patient trust, ensuring data security, and upholding ethical standards. Protecting sensitive medical information from unauthorized access mitigates risks such as identity theft and cybercrime. This paper explores a synergistic approach to safeguarding healthcare data through the integration of Federated Learning, Homomorphic Encryption, and Blockchain technology. Federated Learning enables collaborative model training across multiple healthcare institutions without sharing sensitive data, thereby maintaining patient confidentiality. Homomorphic Encryption complements this by allowing computations to be performed on encrypted data, ensuring privacy throughout the learning process. Blockchain technology further enhances this framework by providing a transparent and immutable ledger of data transactions, reinforcing the integrity and accountability of model updates. Together, these technologies offer a comprehensive solution to privacy challenges in health care, enabling secure data sharing and robust machine learning model development without compromising patient confidentiality. The experimental analysis conducted on the proposed methodology demonstrates its effectiveness and significance in preserving and improving the security of healthcare records in a more reliable and efficient manner.