The mechanical strength, as well as the responsiveness to stimuli, of photosensitive polymers makes these materials exceptionally useful in the reconstruction of hard tissues, although their clinical effectiveness over extended periods of cyclic loading remains unclear. This work systematically reviews 86 research articles that estimate the fatigue life of medical-grade photosensitive polymers using three different techniques: adaptation of the Weibull distribution, Paris–Erdogan crack propagation models, and hybrid statistical–machine learning methods. Data collection comprised standardized bending and tensile fatigue tests performed under physiologically relevant loading conditions. Throughout 2010 to 2024, I found and screened 1247 records while adhering to PRISMA guidelines, eventually yielding 86 studies from sources like PubMed, Google Scholar, and Web of Science. Erdogan models reached accuracy rates of 83–86% (R² range: 0.80–0.84), while the highest performing hybrid statistical–machine learning methods achieved 91–94% accuracy (R² range: 0.88–0.91). These hybrid approaches outshone other models in capturing nonlinear degradation trends in high-cycle fatigue (>10^5 cycles) where traditional models overestimated fatigue life by 12–18%. Regardless of the model used, fatigue resistance was observed to strongly correlate crosslinking density (Pearson’s r = 0.79) and filler composition (r = 0.74). While statistical models have merit, especially during the preliminary stages of design, their predictive accuracy is often limited. In contrast, the machine learning component in hybrid models improved performance under varying load conditions and material complexity, resulting in more clinically reliable estimates of the device lifetime. Key limitations of the study included absent standardized protocols for fatigue testing and difficulties simulating in vivo degradation conditions. Supporting the conclusions of this study are the interdisciplinary efforts required to improve photosensitive polymer fatigue life testing that are guided by real-time clinical predictions and test standardization.