Introduction Brain age prediction using neuroimaging and machine learning has emerged as a promising approach to assess brain health and detect deviations associated with neurological and psychiatric disorders. The difference between chronological age and predicted brain age, known as brain-predicted age difference (brainPAD), is considered a potential biomarker for advanced brain aging. However, most studies rely on single-modality imaging, limiting predictive accuracy and generalization. This study aimed to enhance brain age prediction by integrating multimodal neuroimaging—structural MRI (sMRI) and diffusion MRI-derived fractional anisotropy (FA)—and evaluating its effectiveness in both healthy individuals and schizophrenia patients. Methods We analyzed a large, multi-site dataset of 2,558 healthy individuals (aged 12–88 years) using machine learning approaches to assess the impact of multimodal inputs on brain age prediction. A stacking model combining sMRI and FA features was developed and validated. To evaluate cross-dataset generalization, the model was tested on an independent dataset comprising 56 healthy individuals (aged 20–58 years) and 48 schizophrenia patients (aged 19–65 years). Statistical analyses were conducted to compare brainPAD scores between groups and assess correlations with clinical measures in schizophrenia patients. Results The multimodal stacking model achieved superior prediction performance compared to single-modality models, with a mean absolute error (MAE) of 2.675 years and Pearson’s correlation (r) of 0.970 between predicted and chronological age in the internal test set. External validation on the COBRE dataset demonstrated MAE of 4.556 years (r = 0.877) for healthy controls and 6.189 years (r = 0.873) for patients with schizophrenia. Schizophrenia patients exhibited significantly higher brainPAD scores compared to healthy controls (t = 3.857; p 0.001; Cohen’s d = 0.769), suggesting advanced brain aging. Additionally, brainPAD was significantly correlated with symptom severity scores in schizophrenia (ρ = 0.331–0.337, p 0.05). Discussion Our findings demonstrate that integrating sMRI and FA features improves brain age prediction accuracy and generalization. Furthermore, the correlation between brainPAD and clinical symptoms highlights its potential as a biomarker for disease progression and treatment monitoring. These results underscore the value of multimodal neuroimaging and machine learning in advancing psychiatric neuroimaging research and paving the way for clinical applications in schizophrenia and related disorders. Further investigation with larger sample sizes is required to validate and extend these findings.
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