A new paradigm called cloud federation enables cloud providers to work together and share resources to satisfy consumer needs. However, the reliability of cloud services in a cloud federation environment is still a concern due to the potential risks of failure and downtime. To address this issue, this paper proposes a proactive fault tolerance technique that combines prediction, prevention, and recovery mechanisms to reduce the likelihood of faults and improve the system's resilience to faults. The proposed technique employs machine learning algorithms to predict potential faults and takes proactive measures to prevent them from occurring. In the event of a fault, the technique uses recovery mechanisms to minimize service downtime and restore the system to its normal state. The outcomes of the tests demonstrate that the suggested method can greatly raise the dependability of cloud services in a cloud federation setting.