Snow depth (SD) exhibits high spatiotemporal heterogeneity in Western Himalaya (WH), and its knowledge is essential for applications related to water resources, disaster management, climate, etc. However, due to inclement weather and rugged topographical conditions, only a sparse network of SD monitoring stations exists in WH. Spaceborne passive microwave (PMW) remote sensing datasets provides valuable information about SD; however, only a limited PMW SD studies that cover subregions of WH are available. The current study utilizes Extremely Randomized Trees (ERT) based machine learning technique to estimate daily SD over the entire WH region. The ERT SD model is developed using PMW brightness temperature datasets from Advanced Microwave Scanning Radiometer-2 (AMSR-2), snow cover duration (SCD), and other auxiliary parameters during the winter period between 2012-13 and 2019-20. The data between 2012-13 and 2017-18 is used for training the model, whereas the data between 2018-19 and 2019-20 is used for testing the model. The results demonstrate: (a) The ERT SD model has shown improved SD estimates compared to the available PMW remote sensing-based operational SD products and empirical PMW SD models. (b) in general, with an increase in SD, the mean absolute error of SD retrievals has increased in all SD products/models. (c) Unlike the operational AMSR2 SD product, and Northern Hemisphere Machine Learning SD product, the ERT SD model retrievals have shown better consistency with MODIS snow cover. (d) the developed model has shown a wider range in SD retrievals as compared to other products considered in this study.