Proton exchange membrane fuel cell (PEMFC) durability under dynamic operation remains a major challenge because the observed voltage decay may combine persistent and transient performance changes. This study presents a condition-aware and data-driven analysis of PEMFC degradation under a dynamic fuel cell load cycle (FC-DLC). A public single-cell PEMFC dataset was reconstructed into 3076 dynamic cycles over 1008.24 h of operation and complemented with polarization curves measured directly after dynamic operation and after 12 h of shutdown rest. Load-resolved voltage indicators, polarization descriptors, direct-to-after-rest difference metrics, hysteresis indices, and uncertainty-evaluated short-horizon forecasting models were developed. The dynamic analysis showed that voltage degradation was strongly current-dependent, with the early-to-late voltage drop increasing from 23.09 mV at 0 A to more than 76 mV at the highest current levels. Over the common 100–1000 h comparison window, maximum power decreased by 9.31% in the direct condition and by 12.00% in the after-rest condition, whereas the voltage–current area decreased by 11.79% and 10.40%, respectively. Therefore, the after-rest temporal losses were not uniformly smaller and depended on the selected indicator and current region. The comparison between direct and after-rest curves revealed persistent after-rest minus direct voltage differences of 30–45 mV in medium- and high-current regions even after 1000 h. A sensitivity analysis showed that the voltage-cleaning threshold had no measurable effect on the reported dynamic indicators. For high-load voltage forecasting, Ridge regression achieved RMSE values of 0.00936 V and 0.01134 V at 50- and 100-cycle horizons, improving upon the persistence baseline by 29.7% and 23.5%, respectively. These error reductions were statistically significant, although the corresponding $R^2$ values remained negative on the late-life temporal holdout. The ablation analysis further showed that the complete feature set was not systematically optimal, and the best-performing feature group depended on the target and forecasting horizon. Nominal 90% conformal coverage was adequate at 50 cycles (91.25%) but decreased to 55.03% at 100 cycles, indicating loss of calibration under the longer temporal horizon. Overall, the proposed framework integrates load-dependent voltage-loss characterization, direct and after-rest measurement conditions, feature-group ablation, persistence benchmarking, and uncertainty evaluation without assigning the observed measurement-condition difference to a unique reversible or irreversible mechanism or claiming a validated remaining-useful-life or maintenance-decision system.