Post-harvest losses in the United States are a significant challenge to food availability, economic viability, and agricultural sustainability, and bear implications that extend to both the incomes of farmers and national food security. In this study, we examine how Artificial Intelligence (AI) technologies can be used to mitigate them and reinforce food security in the agricultural environment of the United States. Conducting analysis of post-harvest loss trends over the entire food supply chain, in the specific form of losses in the course of storage in buildings, transportation infrastructure, and processing facilities, this study highlights the necessity of the application of technology in post-harvest loss prevention. Examining varied applications of AI in post-harvest management, including predictive modeling for forecasting deterioration and storage maximizing, quality assessment system-enhancing using Artificial Intelligence for classifying fresh produce, and intelligent supply chain management for logistics optimization and delivery time minimization, this study estimates the impact of these available AI solutions on food security by analyzing improved accessibility of food, economic benefits to stakeholders, and environmental gains in the form of waste reduction and resource optimization. Considering also the challenges of deployment in the form of technology, funding, and knowledge gaps, as well as ethical implications of workforce displacement and data protection concerns, this study also looks for innovative developments in AI and public policies in support of AI in agriculture in the United States. The paper concludes by providing a synoptic view of the revolutionizing impact of AI in minimizing post-harvest losses and enhancing food security in the United States in providing implementable strategies and pointing toward vital directions for further study and techno-developments.