Earth retaining structures are geo-structures designed and constructed to encounter the lateral thrust imparted by backfill soil. Stability assessments of earth retaining structures in the past were performed by accounting homogenous backfill soil due to the ease of Factor of Safety (FoS) determination against a particular failure mode. This deterministic stability assessment dismisses the impacts of spatial soil property variability, thus presenting a restricted interpretation of reality. For in-situ soils, these spatial variations are attributed from inherent variability whereas for relocated backfill geo-materials, nonuniformity in layering and compaction are the main sources invoking variability in soil properties. The complexities due to aforementioned variabilities proliferate extensively under seismic loading conditions. The uncertainties associated with such soil property variability need to be comprehended for attaining a reliable seismic stability assessment of retaining walls. This present study probabilistically explores the implications of geo-material property variability on the sliding stability assessment of a gravity wall subjected to both static as well as pseudo-static loadings. The variability accounted in this study encompasses dry unit weight and friction angle of backfill soil as well as interface friction between wall base and foundation soil. The aforementioned analysis has been performed with the aid of SLOPE/W module of GeoStudio software through limit equilibrium slice approach i.e., Morgenstern-Price method, due to its robustness in accounting both interslice normal as well as shear forces. A parametric assessment has also been performed by altering the statistical parameters of the above-mentioned soil properties, thereby delineating its impact on the sliding stability of gravity wall along with probability of failure and reliability index. This study depicted that accountancy of uncertainties is crucial to obtain reliable stability assessment of gravity wall, which has been attained through the application of probabilistic method in comparison to conventional deterministic approach.