Addressing the pressing challenge of global food security necessitates innovative approaches, especially in the context of a growing world population and unpredictable climate changes. Distributed sensing applications emerged as a promising technological solution, enabling real-time data collection and analysis for informed decision-making within agriculture. This research comprehensively reviews the literature regarding the use of distributed sensing applications to improve food security. Encompassing diverse dimensions, the review appraises sensor types, data acquisition techniques, and the translation of data-driven insights across the agricultural continuum. The author underscored the versatility of distributed sensing in tracking critical parameters like soil moisture, temperature, pest occurrences, and crop development. It further accentuates the transformative role of cutting-edge technologies like the Internet of things (IoT) and data analytics in converting raw sensory data into actionable intelligence. Additionally, the paper discusses issues such as data precision, interoperability, and privacy concerns associated with distributed sensing. The author explored socioeconomic implications and highlighted the necessity of equitable deployment across varied geographies. Drawing from this synthesis, the research identifies prospects such as combining AI for predictive modeling, expanding sensor networks in less developed regions, and fostering synergy between distributed sensing and precision agriculture. This comprehensive review consolidates current insights on distributed sensing applications to advance food security. By distilling key findings, addressing challenges, and envisioning trajectories, this research enhances our understanding of how technology-infused paradigms can strengthen global food production, distribution, and resilience in the face of mounting food security challenges.