Brain age, a biomarker of neurological health, is widely used in neuroimaging for early detection of neurodegenerative diseases. While deep learning models have shown promise in brain age prediction from MRI, data imbalance and model interpretability remain key challenges. This study investigates the impact of data augmentation (DA) on both predictive accuracy and explanation stability in convolutional neural networks (CNNs) for brain age prediction. We compare three training strategies: (i) a baseline model, (ii) a model augmented with real MRI scans from OASIS-3, and (iii) a model trained with synthetic data generated by a diffusion model. Model performance is evaluated using mean absolute error (MAE), while interpretability is assessed through Explainable AI (XAI) methods, including DeepSHAP, Grad-CAM, and Occlusion. Our findings indicate that synthetic augmentation improves predictive accuracy, particularly for underrepresented age groups (individuals aged 40–80 years), while real-data augmentation provides more stable feature attributions. However, differences in XAI methods suggest that explanation reliability varies across training strategies. These results highlight the trade-offs between accuracy and interpretability in AI-driven neuroimaging, emphasizing the need for balanced augmentation strategies to develop clinically trustworthy models.