<p>Soil moisture, the amount of water stored in the soil, is a critical factor influencing agricultural productivity, ecosystem health, and land–atmosphere interactions. However, its spatial and temporal variability is highly nonlinear and scale-dependent, making accurate estimation and prediction a persistent challenge. Traditional physically based and empirical models often require extensive parameterization and struggle to capture large-scale heterogeneity. In contrast, recent advances in machine learning (ML) offer adaptive, data-driven approaches that excel at modeling complex interactions between soil properties, vegetation, and meteorological variables. This review presents a comprehensive overview of ML-based soil moisture modeling, focusing on key methodological advancements, algorithmic classifications, and notable applications across agriculture, hydrology, and climate science. We categorize existing models into time-series, integrated, deep learning, and nonlinear regression frameworks and discuss their strengths, data sources, and limitations. Additionally, we explore emerging research directions, including multimodal data fusion, physics-informed and explainable ML, uncertainty quantification, and transfer learning for cross-climatic zone applications. Finally, the review outlines the future potential of next-generation, physically consistent, and interpretable ML models to improve soil moisture predictions and advance environmental understanding.</p>