Multi-cloud computing is a vitally important topic from both business and technical perspectives since it guarantees resiliency, availability, and security. Due to the vast number of configurations among cloud providers, it is quite challenging to migrate AIOps models across different clouds. Although it is possible to train these models from scratch on the target cloud, this process can be time-consuming and prone to delays. Consequently, the objective of this paper is to create a generalized AIOps model from the original cloud that can be seamlessly applied to target cloud with minimal to zero-shot observations. To achieve this goal, we present a novel framework in this position paper, which harnesses the potential of digital twins to enhance data generalization. Additionally, our proposed framework employs meta-learning techniques to ensure effective model generalization across different cloud environments.