In this research, we address bearing fault classification by evaluating three neural network models: 1D Convolutional Neural Network (1D-CNN), CNN-Visual Geometry Group (CNN-VGG), and Long Short-Term Memory (LSTM). Utilizing vibration data, our approach incorporates data augmentation to address the limited availability of fault class data. A significant aspect of our methodology is the application of neural architecture search (NAS), which automates the evolution of network architectures, including hyperparameter tuning, significantly enhancing model training. Our use of early stopping strategies effectively prevents overfitting, ensuring robust model generalization. The results highlight the potential of integrating advanced machine learning models with NAS in bearing fault classification and suggest possibilities for further improvements, particularly in model differentiation for specific fault classes.