Objectives <p>This study aimed to develop a super lightweight deep learning model for brain age estimation using structural MRI, enabling accurate age estimation with minimal computational cost for deployment in resource-constrained clinical settings.</p> Methods <p>A super lightweight brain age estimation network, termed superLPNet, was proposed. Lightweight convolutional structures inspired by MobileNet were adopted to reduce model parameters and computational burden. Spatial and channel attention mechanisms were further integrated to enhance feature representation without substantially increasing model complexity. The proposed model was evaluated on a combined dataset of 3550 T1-weighted MRI scans and further validated on an independent Alzheimer’s disease (AD) cohort.</p> Results <p>The superLPNet achieved the lowest mean absolute error compared with state-of-the-art models, demonstrating superior brain age estimation accuracy. The number of parameters was reduced by 56.70%–98.75% relative to competing approaches, highlighting its super lightweight design. From a clinical perspective, patients with AD exhibited a significantly larger brain age gap than healthy controls.</p> Conclusions <p>The proposed model enables accurate and efficient brain age estimation using T1-weighted MRI with substantially reduced complexity, supporting its potential for real-world clinical application.</p>