Model-agnostic meta-learning (MAML), coupled with digital twins, is transformative for predictive maintenance (PdM), especially in robotic arms in assembly lines, where rapid and accurate fault classification of arms is essential. Despite gaining significant traction, the framework faces significant challenges, like hypersensitivity to learning parameters and limited generalization during meta-testing. To address these challenges, we proposed an ensemble-based meta-learning approach integrating majority voting with model-agnostic meta-learning (MAML), and operational grouping was implemented via Latin hypercube sampling (LHS) to enhance few-shot learning ability and generalization along with maintaining stable output. This approach demonstrates superior accuracy in classifying a significantly larger number of defective mechanical classes, particularly in cross-domain few-shot (CDFS) learning scenarios. The proposed methodology is validated using a synthetic vibration signal dataset of robotic arm faults generated via a digital twin. Comparative analysis with existing frameworks, including ANIL, Protonet, and Reptile, confirms that our approach achieves higher accuracy in the given scenario.