Background <p>Migraine chronification remains difficult to characterize at the individual level, and robust baseline imaging markers associated with subsequent conversion from episodic migraine (EM) to chronic migraine (CM) are lacking. Resting-state functional MRI (rs-fMRI) dynamic functional connectivity (dFC) captures transient brain network interactions, but conventional approaches provide limited interpretability for clinical translation.</p> Methods <p>We enrolled 195 participants at baseline, including 95 healthy controls (HC) and 100 patients with episodic migraine at baseline. All migraine patients underwent baseline rs-fMRI during the interictal phase and were followed for 12 months; 70 remained EM (non-converters) and 30 converted to CM. Sliding-window dFC matrices (55-TR window, 2-TR step) were constructed from 142 regions of interest and represented as temporal graph sequences. We developed an interpretable temporal graph neural network integrating a two-layer graph isomorphism network, a gated recurrent unit, and dual spatial–temporal attention mechanisms to quantify node-level (“where”) and window-level (“when”) importance. A multilayer perceptron performed three-class discrimination among HC, EM non-converters, and CM converters. Model performance was evaluated using stratified ten-fold cross-validation. Edge-wise ANCOVA controlling for age, sex, and mean framewise displacement with false discovery rate correction was applied to dFC matrices at key temporal-attention windows and within k-means-derived dFC states.</p> Results <p>Four recurrent dFC states were identified. State 3 showed the highest fractional occupancy and mean dwell time and was the only state with significant between-group edge-wise differences after correction. Compared with HC, migraine groups exhibited altered connectivity involving sensory, attention, default-mode, and subcortical systems. CM converters showed additional baseline abnormalities involving sensorimotor–visual, default-mode, ventral attention, and limbic-related circuits. Spatial attention shifted from occipital–frontoparietal hubs in HC to frontal–insular prominence in EM non-converters and a frontoparietal–limbic configuration in CM converters. Temporal attention peaked at window 23, where chronification-related abnormalities were most evident. The model achieved AUCs of 0.817(HC), 0.832(EM non-converters), and 0.874(CM converters), with a macro-AUC of 0.841 (95% CI 0.77–0.91). Findings were robust across alternative window lengths and step sizes.</p> Conclusions <p>This rs-fMRI dFC–based temporal graph learning framework identified baseline spatiotemporal network patterns associated with subsequent migraine chronification and generated individualized time-resolved importance maps. Although the model showed promising internal discriminative performance, these findings should be interpreted as risk-related pattern identification rather than as evidence of a clinically validated prediction tool. External prospective multicenter validation is required before clinical translation.</p>