India, particularly the northern regions like the Himalayas, is prone to frequent avalanches due to heavy snowfall, steep terrain, and climatic conditions. In recent years, India has witnessed several deadly avalanches, such as the October 2022 Draupadi ka Danda avalanche in Uttarakhand, which claimed the lives of 27 climbers, and the February 2021 glacier burst and avalanche in Chamoli, which led to over 200 casualties. Snow depth measurement is critical for predicting avalanches in mountainous and polar regions. Recent advancements in electromagnetic wave technologies, such as radar and microwave sensing, have revolutionized the accuracy and scalability of snow depth monitoring. This paper provides a comprehensive review of state-of-the-art techniques used in snow depth measurement, focusing on systems utilizing electromagnetic waves across the microwave, millimeter-wave, and radar spectra. Key technologies discussed include Frequency-Modulated Continuous Wave (FMCW) radar operating at 60 GHz, which offers high-resolution snowpack characterization, particularly for localized measurements [1]. Synthetic Aperture Radar (SAR) systems utilizing C-band frequencies, in conjunction with deep learning models, have effectively estimated snow depth in complex terrains such as forested areas, offering hope for improved avalanche prediction [2]. Airborne multi-channel ultra-wideband (UWB) FMCW radar systems demonstrate the capability for large-scale, high-precision snow depth monitoring, particularly in remote regions, providing a promising solution for challenging terrains [3]. Passive microwave radiometry, integrated with machine learning algorithms like random forest model, further enhances snow classification and depth prediction on a broader scale, paving the way for more accurate and scalable snow depth monitoring [4]. Uncertainty in snow depth measurement remains a challenge, particularly in complex environments. However, ongoing research into radiative transfer models and signal processing techniques shows promise in reducing errors [5]. This review synthesizes findings from diverse approaches, highlighting the strengths, limitations, and future directions for improving snow depth measurement accuracy. By integrating these technologies and methods, future snow depth measurement systems can provide more reliable, real-time data across various terrains, contributing to better hazard prediction. Abstract should summarize the contents of the paper in short terms, i.e. 150–250 words.