Federated Learning (FL) enables collaborative training across distributed clients while preserving data privacy. Non-independent and identically distributed (non-IID) data poses a significant challenge, causing inconsistent local updates and degrading global model performance. We propose FedCWE, a federated cluster-based weight sampling and ensemble learning algorithm. FedCWE clusters clients based on data heterogeneity and volume, applies weighted sampling, and uses ensemble learning to enhance the global model. Experiments on natural and medical image datasets show FedCWE improves accuracy by 2%–5% over state-of-the-art FL methods and up to 10% in extreme non-IID scenarios, while reducing communication costs.