Surface soil moisture (SSM) is a vital variable for irrigation management, estimation of crop water stress, and agricultural drought management. This systematic review integrates recent progress (2019-2024) in remote sensing-based SSM estimation, based on 116 peer-reviewed articles selected using PRISMA guidelines. The review indicates that multi-sensor techniques, integrating optical, radar, and climate information coupled with machine learning (ML) and data assimilation methods, have immensely enhanced the spatial and temporal resolution of SSM products. These developments have brought SSM retrieval within the realm of useful, field-scale application for agricultural water management. Hybrid models and AI-downscaled approaches, in particular, have a very high potential for operational decision-making across varying agro-ecologies. Trends in performance, regional research gaps, and areas for improvement in terms of data coverage, especially for semi-arid and smallholder-dominated landscapes, are also addressed in this review. SWOT analysis of prominent retrieval algorithms identifies their advantages and limitations in application, revealing the compromises between complexity, scalability, and accuracy. Although there has been increasing technical development, with few exceptions, there is no large-scale application in actual irrigation systems. The article ends by placing greater emphasis on enhancing stronger validation protocols, improved application within crop and hydrological models, and region-tailored modifications of retrieval workflows. In the future, new satellite missions and enhanced ground data infrastructure offer opportunities to enhance the contribution of SSM to climate-resilient agriculture. This review offers a timely basis to advance SM monitoring systems that are not only scientifically valid but operationally pertinent to sustainable water management in agriculture.