Altered iron levels, detected using iron-sensitive MRI techniques such as quantitative susceptibility mapping (QSM), are observed in neurological disorders and may play a crucial role in disease pathophysiology. However, brain iron changes occur slowly, even in neurological diseases, and can be influenced by physiological or environmental factors that are difficult to quantify in the research or clinical settings. Therefore, novel analysis methods are needed to improve sensitivity to disease-related iron changes beyond conventional region-based approaches. This study introduces IRONMAP, Iron Network Mapping and Analysis Protocol, which is a novel network-based analysis method to evaluate over-time changes in magnetic susceptibility. With this technique, we analyzed short-term (<1 year) longitudinal QSM data from a cohort of people with multiple sclerosis (pwMS) and healthy controls (HCs) and assessed disease-related network patterns, comparing the new approach to a conventional per-region rate-of-change method. IRONMAP revealed over-time, MS-related brain iron abnormalities that were undetectable using the rate-of-change approach. IRONMAP was applicable at the per-subject level, improving binary classification of pwMS vs. HCs compared to rate-of-change data alone (areas under the curve: 0.773 vs. 0.636, p = 0.024). Further analysis revealed that the observed IRONMAP-derived HC network structure closely aligned with simulated networks based on healthy aging-related susceptibility data, suggesting that disruptions in normal aging-related iron changes may contribute to the network differences seen in pwMS. IRONMAP is applicable to various neurological diseases, including Alzheimer’s disease and Parkinson’s disease, and can be used between any set of brain regions. Our proposed technique may allow for the study of brain iron abnormalities over shorter timeframes than previously possible.