Sensitive domains such as healthcare institutions are increasingly relying on Federated learning for data security. Irrespective of this approach, they are gullible to adversarial attacks such as poisoning attacks and confidentiality breaches. To overcome these hindrances, Blockchain driven Federated learning is put forward, which integrates Secure Multi-Party Computation (SMPC) with Zero-Knowledge Proofs (ZKPs). This framework strives to ensure confidentiality in a distributed training environment. The individual entities train their local AI models with their exclusive datasets and generate Zero Knowledge Proofs to assert the accuracy of the model updates. The SMPC protocol encrypts the model updates, which are later aggregated to enable computing that guarantees privacy. Later, Smart Contracts are used to immutably store these adjustments on the Blockchain ledger, ensuring impenetrable model ensemble. To improve the trade-off between model dependability and precision, privacy noise is dynamically adjusted by employing adaptive differential privacy, based on individual client’s reputation. Extensive experiments prove the fact that the proposed sys-tem prominently reduces computing overhead in comparison to the established system while strengthening the attack detection rates. This architecture establishes a benchmark for information security in delicate areas like healthcare systems while designing its data-sensitive Al models.