As a distributed method of model training, Federated Learning (FL) does not have to store raw data centrally but is prone to gradient leakage, model poisoning and single-cloud dependency. Blockchain-based FL mechanisms enhance trust and auditability, whereas differential privacy (DP) ensures safe and confidential client data; nevertheless, they are not usually combined into a full-scale and multi-cloud-based solution. The paper presents a Blockchain-Embedded Federated Learning Framework with Differential Privacy in Multi- Cloud Environments to ensure the provision of decentralized trust management, mathematically assured privacy, and high availability. It has a framework with a DP-based gradient perturbation mechanism, a blockchain-based verification and immutable logging, a strategy of multi-cloud aggregation that spreads the computation over independent cloud providers, ensuring that there are no single points of failure. Evaluation on MNIST and CIFAR-10 experiments show that the proposed system is more robust and can withstand faults and inference and poisoning attacks with competitiveness in the accuracy. The findings indicate that there is a decrease in the aggregation latency by 31 percent caused by the multicloud parallelization and an increase in the model integrity caused by the blockchain consensus. In general, the suggested architecture provides a secure, scalable and privacy preserving framework to real-world collaborative learning over heterogeneous and decentralized cloud infrastructures.