<p>Adaptive immunity is a central defence system essential for long-term and highly specific protection against pathogens through the precise molecular recognition of antigens by lymphocytes. However, predicting how mutations reshape these interactions remains a major challenge. Although previous computational approaches leverage large-scale pretraining for mutation-effect predictions, most are designed for specific tasks or modalities and struggle to generalize across the heterogeneous, multimodal landscape of immune recognition. Here we introduce UniAIR, a modular, multimodal framework for the accurate and generalizable prediction of mutation effects across immune recognition scenarios. UniAIR integrates a standardized data pipeline, an interface-centric sequence–structure fusion transformer that integrates evolutionary information with geometric representations, and a suite of extensions for multiexpert consensus and adaptation to predicted structure inputs. We comprehensively evaluated UniAIR through large-scale benchmarking and independent tests across immunological tasks. The evaluation covered both extracellular and intracellular immune recognition, including antibody maturation, antigen escape, TCR–pHLA optimization and analyses in which experimental structures were unavailable. Extensive experiments show that UniAIR achieves state-of-the-art performance and delivers robust predictions with minimal task-specific tuning. In particular, UniAIR successfully performed multiround peptide optimization of a TCR–pHLA complex under sparse feedback and identified key functional mutations in incomplete antibody–antigen structures. Together, UniAIR establishes a unified computational foundation for mapping mutation landscapes, advancing understanding of adaptive immune recognition and accelerating immunotherapeutic design.</p>