Understanding the aging process of the human brain is crucial for elucidating its impact on cognitive functions and neurological health. Magnetic Resonance Imaging (MRI) serves as a valuable tool for investigating age-related structural changes in the brain, particularly in estimating brain age—a measure derived from structural characteristics to approximate chronological age. Deviations between estimated brain age and chronological age, termed the brain-age gap (BAG), provide insights into variations in brain development and aging, with implications for cognitive impairments and neurological disorders. This study focuses on the thalamus and its nuclei, a brain region pivotal for information processing, where pathological changes are often observed in Parkinson's disease (PD) patients. Leveraging Explainable Artificial Intelligence (XAI) principles, the study proposes a novel interpretable Machine Learning (ML) diagnostic approach utilizing thalamic nuclei volumes from MRI to estimate BAG in PD patients. Specifically, Explainable Boosting Machines (EBM), a glass-box model based on Generalized Additive Models (GAMs), is employed to optimize accuracy and interpretability. Using data from the Parkinson's Progression Markers Initiative (PPMI), the study demonstrates the effectiveness of EBM in accurately estimating BAG, with specific thalamic nuclei identified as key predictors. Moreover, the study reveals a heterogeneous pattern of hypertrophy and atrophy in thalamic nuclei volumes in PD patients, with clinical implications for predicting disease progression and severity. These findings underscore the potential of EBM in improving early diagnosis, prognostication, and treatment monitoring of PD, ultimately enhancing patient outcomes and guiding personalized therapeutic interventions.