The growing challenges of climate change, resource scarcity, population growth, and food security increase the need for innovative approaches to agricultural management and decision-making. Artificial intelligence (AI) and data science transform the agricultural sector by enabling data-driven insights, predictive analytics, and intelligent automation that support more resilient and sustainable farming practices. Through the integration of advanced technologies like machine learning, remote sensing, Internet of Things (IoT) devices, and big data analytics, organizations can make informed decisions that optimize productivity, reduce environmental impacts, and enhance resource efficiency. AI and data science-driven decision-making for sustainable agriculture reveals new methods, applications, and emerging trends that shape the future of smart, adaptive, and environmentally responsible agricultural systems. AI and Data Science-Driven Decision-Making for Resilient and Sustainable Agriculture explores how advanced AI and data science methodologies can support robust, transparent, and adaptive decision-making across agricultural systems facing increasing uncertainty and systemic stress. It covers foundational frameworks, advanced AI and statistical methods, multi-scale system integration, governance and resilience assessment, and real-world case studies. This book covers topics such as smart farming, data science, and supply chains, and is a useful resource for engineers, agriculturalists, academicians, researchers, and scientists.