This keynote explores the growing need for probabilistic methods in assessing the stability of geosynthetic-reinforced soil retaining structures, under seismic and uncertain field conditions. While traditional deterministic approaches remain widely used, they often overlook the natural variability in soil properties, environmental effects like partial saturation, and complex seismic forces. Probabilistic analysis offers a more realistic way to account for these uncertainties and make safer, better-informed design decisions. The keynote brings together insights from recent research, including surrogate-based modeling techniques like the stochastic response surface method and fourth-moment normal transformation, which help balance accuracy and efficiency in high-dimensional or nonlinear problems. Case studies, such as the Northridge earthquake, demonstrate the practical use of these tools in real-world situations. The keynote also reflects on challenges in choosing parameters like the coefficient of variation, the role of damping and frequency in seismic stability, and the practical limitations of software and field data. Looking ahead, it suggests pathways for integrating probabilistic tools into everyday engineering practice such as combining machine learning with physics-based models, developing partial safety factors from rigorous simulations, and creating open-source toolkits for field use. Making these tools accessible and practical is key to improving the reliability and safety of reinforced soil structures amid real-world uncertainties.