Abstract
This chapter offers a comprehensive review of recent developments in data science and artificial intelligence (AI) within agricultural supply chains, emphasizing supply optimization and demand forecasting. It introduces a novel framework that maps AI and data science applications across various supply chain domains, offering structured insights into current implementations and untapped opportunities. Unlike previous works, this study synthesizes findings from the last decade of peer-reviewed literature and conference proceedings, presenting an integrated analysis of technologies such as big data analytics, machine learning, and predictive modeling. The review highlights significant improvements in forecasting accuracy, operational efficiency, and resource management. However, it also identifies ongoing challenges related to data quality, infrastructure limitations, and workforce skill gaps. To overcome these barriers, the chapter recommends targeted investments in infrastructure, research, and professional training.