Breast cancer is the prevailing form of cancer among women on a global scale. It originates in the cells of the breast tissue. It poses a significant threat because of its invasive character and its ability to metastasis. It is very difficult to diagnose it in early stages because there may be no outward signs or symptoms. Prompt diagnosis and treatment are crucial for improving survival rates and mitigating its hazardous impact on patients’ lives. In this situation, a novel technology is much needed for its precise and confidential detection. Traditional methods of early diagnosis necessitate the gathering of personal data, thereby giving rise to significant concerns regarding privacy. Recently, emerging technologies such as machine learning, federated learning, and blockchain are showing greater importance in medical field along with ensuring the privacy of data. This chapter presents methods for the diagnosis of breast cancer using federated machine learning and blockchain technology. Federated machine learning enables collaborative model training across many healthcare facilities while protecting the privacy and security of patient information. In order to meet legal standards and promote trust among all parties involved, these models provide knowledge sharing without compromising patients’ privacy. The chapter illustrates the incorporation of blockchain technology to improve data integrity and tamper resistance. Because of its decentralized and transparent nature, the blockchain reliably keeps track of model updates and training parameters, allowing all parties to validate the model’s training history and so establishing the reliability of the shared knowledge. Incorporating federated learning with blockchain technology not only protects user privacy but also improves the accuracy and robustness of breast cancer detection models. Finally, the chapter delves into the potential applications of federated machine learning and blockchain technology in the context of breast cancer detection and its revolutionary effect on patient care, earlier diagnosis, and better treatment outcomes.