T cell immunogenicity, the ability of peptide fragments to elicit T cell responses, is a critical determinant of the safety and efficacy of protein therapeutics and vaccines. While deep learning shows promise for in silico prediction, the scarcity of comprehensive immunogenicity data is a major challenge. We present T cell immunogenicity scoring via cross-domain aided predictive engine (T-SCAPE), a novel multidomain deep learning framework that leverages adversarial domain adaptation to integrate diverse immunologically relevant data sources, including major histocompatibility complex (MHC) presentation, peptide-MHC (pMHC) binding affinity, T cell receptor-pMHC interaction, source organism information, and T cell activation. Validated through rigorous leakage-controlled benchmarks, T-SCAPE demonstrates exceptional performance in predicting T cell activation for specific peptide-MHC pairs. It also accurately predicts the antidrug antibody-inducing potential of therapeutic antibodies without requiring MHC inputs. This success is attributed to T-SCAPE's biologically grounded and data-driven multidomain pretraining. Its consistent and robust performance highlights its potential to advance the development of safer and more effective vaccines and protein therapeutics.