One of the main challenges in machine learning is providing understandable explanations for complex models. Despite outperforming humans in many tasks, machine learning models are often treated as black boxes that are difficult to interpret. Post-hoc explanation methods have been developed to create interpretable surrogate models that explain the behavior of black-box models. However, these methods have been shown to perpetuate bad practices and lack stability. Recently, inherent explainable approaches have been proposed to provide built-in explainability to models. However, most of these methods sacrifice performance. This paper proposes the Neural Architecture Search for Explainable Networks (NASXNet) approach to address the trade-off between performance and interpretability. Our method applies architecture search to generate high-performance and explainable neural networks for image classification tasks. We conduct experiments on four datasets: CUB-200-2011, Stanford Cars, CIFAR 10, and CIFAR 100. The results demonstrate that our models provide a high-level interpretation of prediction results, achieving state-of-the-art performance that is on par with non-explainable models. This paper contributes by solving the trade-off problem between performance and interpretability. It is the first to apply neural architecture search to develop explainable deep learning models, generating state-of-the-art explainable models that outperform existing approaches. Additionally, a new training process is proposed that enables faster convergence during model training.