This study presents the integration of the physically based SNTHERM model with machine learning methods to simulate and analyze the seasonal dynamics of the snowpack in the foothill zone of East Kazakhstan. The modeling framework was applied to the case of the Shemonaikha meteorological station, located in a flood-prone area with a sharply continental climate. Meteorological forcing was provided by hourly ERA5-Land reanalysis data, enabling high-resolution simulation of snow accumulation, metamorphism, and melt processes throughout the 2022–2023 winter season. The model outputs-including snow water equivalent (SWE), snow depth, and temperature profiles-were interpolated to daily resolution and analyzed to extract key snow cycle indicators. These included the date of maximum SWE, the onset of sustained melt under positive air temperatures, and the timing of snowpack disappearance. Time series analysis and machine learning techniques were used to identify and quantify melt phase characteristics. Results demonstrate that the SNTHERM model, when combined with data-driven analysis, effectively captures the snowpack evolution at Shemonaikha and offers a promising approach for snowmelt modeling and hydrological risk assessment in similar continental environments.