Artificial Intelligence in Immunoengineering: Methods, Models, and Translational Applications examines how AI reshapes immunoengineering to design immune-modulating biomaterials, imaging systems, and diagnostic tools. It presents an integrated view of data-driven approaches that accelerate discovery and translation in biomedicine. Convergence is critical to the success of immunoengineering, and drawing on fields outside of its largely biomaterials-based beginnings, to areas such as biomedical data science, computational medicine, and public health, quickening its progress and broadening its impact. This book surveys biological data from single-cell to spatial omics and proteogenomics, and offers practical machine learning frameworks, generative and mechanistic models, and multimodal integration strategies. It addresses bench-to-bedside translation, validation pipelines, and regulatory considerations for AI-enabled immunoengineering tools. The book highlights the rapid progress of multidisciplinary research within immunotherapy and immunoengineering. Looking forward, the prospects of immunoengineering appear promising, with further advancements in disease prevention, diagnostics, and treatment on the horizon. Graduate students, academic researchers, clinician-scientists, and computational biologists gain standardized workflows, mechanism-aware modeling, and reproducible pipelines that advance immunotherapies and diagnostics from concept to clinical impact.