This study evaluates the performance of time and frequency-domain fatigue life assessment methods applied to various synthetic and real-world loading spectra, including block, Gaussian, and mission-derived datasets such as FALSTAFF, TWIST, and WISPER. Using stress cycle extraction via Rainflow counting and damage estimation models like Palmgren–Miner, Dirlik, and Zhao–Baker, the investigation highlights the strengths and limitations of each approach. Results indicate that time-domain methods are more suitable for complex, non-stationary loadings, providing detailed cycle information critical for safety assessments, whereas frequency-domain models offer efficiency advantages for simpler, stationary spectra. The findings emphasize the importance of selecting appropriate methods based on load spectrum characteristics, with prospects for hybrid and machine learning-based enhancements to improve accuracy in complex loading scenarios.