Ongoing climate warming is impacting the frequency and magnitude of extreme weather. The high sensitivity of snow to changes in temperature and precipitation makes it a primary indicator of climate change. Previous studies have proven that the snow cover extent has decreased with rapid warming. Nevertheless, this remains controversial, and no solid conclusion has been reached regarding snow depth changes. Numerous remote sensing‐based approaches have been used to derive spatially continuous snow depth. However, challenges remain in capturing and understanding the spatial variability of snow depth because of the non-linear and so-called ‘ill-posed’ problems associated with inversion framework. Machine learning (ML) techniques (including deep learning) are beginning to play important roles in advancing snow depth retrieval with microwave remote sensing, owing to their strong ability to fit nonlinear, nonexplicit functional relationships between snow depth and massive amounts of geoscience data. However, a systematic review of ML applications in snow depth retrieval with remote sensing is notably absent from the literature, and the trajectory for future advancements remains ambiguous. This review comprehensively summarizes the implementation and progress of snow depth research using microwave remote sensing over the last decade (2015–2025), and analyzes current research directions and areas where further developments are needed. An analysis of the literature reveals that the number of ML-related articles has increased over the past 10 years, rising from 3 to 33. By first-author affiliation, China and the United States lead in terms of contributions, accounting for almost 70 % of papers. We also found that western countries are actively engaged in high-resolution snow depth retrieval (ranging from meter to hundreds of metres) at regional or catchment scales (especially over mountains), which is attributed to their dense and comprehensive ground-based and airborne field campaigns (e.g., SnowEx, NoSREx, and ASO Lidar etc.). While China focuses on snow depth retrieval at the global scale or regional scales, typically at a coarse spatial resolution (10 or 25 km) or spatial downscaling (1 km or 500 m). Our decadal review concludes with five existing paradigms, namely, the coupling of ML and snow physical model (snow electromagnetic model or process model); developing snow electromagnetic models for simulating microwave signals in assimilation or iteration algorithms; optimizing snow electromagnetic models by providing key inputs or accelerating operational efficiency; improving existing gridded snow depth products by data fusion, bias correction or assembly techniques; and downscaling coarse snow depth products to a fine-scale resolution. However, some challenges and unresolved issues still exist. Our future efforts should aim to bridge the disparity in model–observation mismatch, integrate fundamental physical laws into ML structures, enhance the quality of ML training samples, and improve snow depth estimates under complex conditions (e.g., in mountainous and polar regions and during the snowmelt season). This paper provides a comprehensive review of the applications of ML techniques in snow depth remote sensing, focusing on current paradigms, existing challenges, and potential future research directions. We believe that ML techniques hold significant potential for addressing the challenges associated with the quantitative inversion of snow depth and deepening our understanding of the spatial variability of the snowpack globally.