The accurate quantification of brain age from MRI has emerged as an important biomarker of brain health. However, existing approaches are often restricted to narrow age ranges and single-modality MRI data, limiting their capacity to capture the coordinated macro- and microstructural changes that unfold across the human lifespan. To address these limitations, we develop a multi-modal brain age framework to characterize the integrated evolution of brain morphology and white matter organization. Our model adopts a two-stage architecture, where modalities are processed independently and integrated via late fusion in both stages: first to estimate a probability distribution over six developmental stages, and then to predict age via probability-weighted stage-specialized experts. Experiments on nine datasets spanning fetal to elderly stages demonstrate competitive in-domain performance and out-of-domain generalization, with our method reducing MAE by 13% and 78% over existing baselines and multi-modal integration yielding 12-13% gains. Analysis of ADNI clinical groups further suggests the potential of the predicted brain age gap to characterize Alzheimer's-related brain aging.

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