Vaccines are among the most effective medical interventions, yet substantial heterogeneity remains in both immunogenicity and reactogenicity across individuals, age groups, and clinical settings. Systems vaccinology has helped address this challenge by integrating transcriptomics, immunology, and computational analysis to identify early molecular signatures associated with vaccine induced protection. More recently, advances in artificial intelligence, machine learning, single cell RNA sequencing, and spatial transcriptomics have expanded this framework, making it possible to move from descriptive correlates toward more precise and predictive models of vaccine response. In this talk, I will discuss how multimodal data can be used to characterize the cellular and tissue level mechanisms that shape vaccine induced immunity. I will highlight how insights and analytical approaches derived from single cell and spatial transcriptomics studies of infectious diseases can help reveal immune heterogeneity that is often missed in bulk analyses, including rare cell states, tissue niches, and coordinated response programs associated with effective or dysregulated immunity. I will also present how machine learning models can integrate these data layers with vaccinology datasets to predict immunogenicity and adverse events, with the goal of improving vaccine design, stratification, and monitoring.