GNSS-IR enables the extraction of environmental parameters such as snow depth by analyzing signal-to-noise ratio, indicating the strength of the GNSS signal. We propose a machine learning (ML) classification approach for snow depth retrieval using the GNSS-IR technique. ML classifier algorithms were studied to classify the strong and weak ground reflections using input parameters (azimuth angle, satellite elevation angle, day of year, amplitude of reflected signal, epoch number, etc.) as independent variables. GPS data collected by UNAVCO AB39 and daily snow depth data from SNOTEL Fort Yukon for a 6-year period (2015-2020) were considered. The first 4-year data were trained by some well-known ML classifiers to weight the input data and then used to classify the strong and weak signals. Tree-based classifiers, Random Forest, AdaBoost, and Gradient Boosting overperformed the other classifiers since they have more than 70% accuracy, so we performed our analysis with these three methods. The last 2-year data were used to validate both trained models and snow depth retrievals. The results show that ML classifier algorithms perform better results than traditional GNSS-IR snow depth retrieval; they improve the correlations by up to 19%. Moreover, the root-mean-square errors decrease from 15.4 to 4.5 cm. This study has a novel approach to the use of ML techniques in GNSS-IR signal classification, and the proposed methods provide a critical improvement in accuracy compared to the traditional method.