Immunoinformatics encompasses the computational analysis, modeling, and prediction of immune system processes using large-scale biological and clinical datasets. This field integrates immunology with informatics to develop algorithms, databases, and prediction tools that help decode complex immune responses at the molecular and cellular levels. Using techniques such as epitope prediction, structural modeling, machine learning, and network-based analysis, immunoinformatics enables the identification of vaccine targets, characterization of antigen–antibody interactions, and prediction of T-cell and B-cell epitopes with high accuracy. These computational approaches support the development of personalized vaccines, immunotherapies, allergy assessments, and autoimmune disease diagnostics. Immunoinformatics also plays a critical role in understanding host–pathogen interactions, mapping immune diversity, and accelerating rapid vaccine design during emerging infectious disease outbreaks. As immunological data continue to expand through high-throughput sequencing and single-cell technologies, robust computational pipelines, curated immunological databases, and secure data-sharing frameworks are essential for translating immune-related data into actionable biomedical and clinical insights.