Seismic events pose a significant threat to the stability of slopes, especially in railway embankments. Embankments are the critical civil infrastructure in a transportation network. These structures may experience severe displacements and damage due to dynamic loading from earthquakes. The dynamic behavior and stability of such embankment slopes under earthquake loading must be assessed for their safety. This paper presents a series of deterministic and probabilistic analyses of embankment slopes under the framework of limit-equilibrium-based methods along with Monte Carlo simulation. The deterministic stability analysis employed three limit equilibrium methods: the Morgenstern-Price method, Bishop’s method, and Spencer’s method, utilizing Slide 2 software. The probabilistic analysis has been performed using Monte Carlo simulation. The variable soil properties of the embankment have been considered in the analyses by varying the coefficients of variation (COV) of cohesion, angle of internal friction, and unit weight. The four distinct datasets of different COVs have been used in the Monte Carlo simulation. The unit weight is modeled using a lognormal probability distribution function, whereas the cohesion and the angle of internal friction of soil are modeled using a normal probability distribution function. Height and the slope’s angle are two geometric parameters that are regarded as constants. The pseudo-static analyses with varying seismic coefficients in horizontal (Kh) directions have been performed to estimate the dynamic behavior of the embankment. Further, results of static analysis and pseudo-static analysis of embankment for different values of seismic coefficients (Kh) have been compared. From this study, it has been found that the angle of shearing resistance and cohesion are more sensitive parameters compared to the unit weight of the soil. The factor of safety of the embankment was reduced by 25–65% under dynamic conditions compared to that in static conditions. It has also been concluded that the probability of failure may reach 100% for a Kh value of 0.4 under dynamic conditions. The findings not only advance the understanding of probabilistic slope stability analysis techniques but also offer practical implications for engineers and geotechnical professionals dealing with slope stability assessment and risk mitigation.