Post-harvest losses remain one of the most pressing challenges in global agriculture, undermining food security, farmer livelihoods, and the resilience of value chains. Traditional supply systems often lack the real-time data and analytical frameworks needed to monitor crop quality and environmental conditions across storage, transportation, and distribution phases. Recent advances in the Internet of Things (IoT) have introduced sensor networks capable of capturing continuous, high-resolution data on parameters such as temperature, humidity, and ethylene concentration, which are critical for maintaining crop integrity. However, the full potential of IoT lies not only in data collection but in its integration with predictive modeling techniques that enable proactive decision-making. Predictive models, ranging from statistical approaches to machine learning algorithms, can analyze sensor data streams to forecast spoilage risks, optimize logistics, and guide intervention strategies before losses occur. By embedding these systems within agricultural value chains, stakeholders can achieve more transparent, adaptive, and sustainable operations that minimize waste and enhance profitability. The convergence of IoT and predictive analytics also supports broader sustainability goals, including reducing greenhouse gas emissions associated with food waste and promoting resource-efficient practices. While technical challenges remain such as interoperability, scalability, and cost-effectiveness emerging case studies demonstrate significant reductions in losses when these technologies are applied cohesively. This approach not only safeguards food quality but also strengthens trust and efficiency across producers, distributors, and consumers, thereby advancing more resilient and sustainable agricultural value chains. The integration of IoT sensor networks with predictive modeling represents a transformative pathway for addressing food system vulnerabilities in both developing and developed economies.