Multimodal Artificial Intelligence for Intelligent Quality Assessment presents a comprehensive exploration of emerging Artificial Intelligence technologies for developing intelligent, reliable, and automated quality-assessment systems. The book focuses on the integration of diverse data sources such as images, video, text, audio, sensor measurements, spectral information, and environmental data to achieve a more comprehensive understanding of product and process quality. It covers important topics including multimodal data fusion, Vision Transformers and foundation models, Explainable AI, Edge AI, Federated Learning, and Blockchain–IoT–AI-based quality traceability. The book also examines practical applications of these technologies in agriculture, food processing, manufacturing, healthcare, and other domains. A major focus is placed on intelligent rice quality assessment and the development of a Smart Rice Mill integrating Artificial Intelligence, IoT, computer vision, Edge computing, and blockchain for real-time monitoring, quality prediction, process optimization, and traceability. The book further discusses challenges such as data heterogeneity, model generalization, computational complexity, privacy, security, sensor reliability, and real-world deployment. It concludes by highlighting future directions including autonomous processing, digital twins, federated learning, edge-cloud collaboration, blockchain-enabled traceability, smart packaging, and digital quality certification. This book is intended to serve as a useful reference for researchers, academicians, students, technology professionals, agricultural technologists, and industry practitioners interested in the practical application of multimodal Artificial Intelligence for trustworthy and intelligent quality assessment.