Predictive maintenance (PdM) in robotic arms is essential to reduce downtime and ensure efficiency in industrial assembly lines. Model-Agnostic Meta-Learning (MAML), combined with digital twins, offers a promising approach for rapid fault identification and classification. However, existing MAML-based approaches suffer from challenges such as hypersensitivity from learning parameters along with limited generalization in testing domain. To address these limitations, we propose an ensemble-based meta-learning approach that integrates majority voting with MAML and operational grouping strategies. This method enhances few-shot learning capabilities, improves generalization, and stabilizes model performance across varying conditions. Our framework is validated using a synthetic vibration signal dataset generated via a digital twin, simulating different robotic arm faults. The proposed approach demonstrates higher accuracy in classifying a broader range of defective mechanical classes, specifically in cross-domain few-shot (CDFS) learning settings. Comparative analysis with alternative meta-learning frameworks, including Reptile, Protonet, and ANIL, confirms the effectiveness of our approach. By leveraging ensemble-based learning, we achieve improved robustness and higher classification accuracy, making our method a viable solution for real-world PdM applications in industrial robotics. The integration of digital twins further enhances the model’s reliability, bridging the gap between simulation and real-world deployment. Additionally, this approach reduces data dependency, allowing effective fault classification even in scenarios with limited labelled data, making it highly adaptable to dynamic industrial environments.