Quantum machine learning algorithms fall short of their classical counterparts in terms of accuracy, which leads to lack of established advantages for real-world application. A range of quantum machine learning algorithms have been proposed and have shown superior performance to classical algorithms, but only in limited settings. This paper introduces a novel approach to quantum machine learning classifiers, leveraging a mixture of experts model. The proposed method not only enhances the accuracy of the classifiers but also broadens their applicability by enabling classification across multiple datasets and tasks. The MoE model demonstrates strong performance, achieving 93% accuracy on the Reduced Titanic dataset and a MAPE of 99 on the Health Insurance data, outperforming the individual quantum models. It also shows the highest classification score for a MNIST digit pair (100%) and competitive results on Iris. Our proposed method outperforms conventional quantum machine learning models and substantially improves classifier generalizability by integrating the strengths of various quantum models.