The rapid deployment of vaccines during global health crises, such as the COVID19 pandemic, has brought renewed attention to the importance of monitoring post-vaccination adverse reactions. While traditional pharmacovigilance systems rely on manual reporting and statistical surveillance, the emergence of artificial intelligence (AI) offers powerful tools for predictive modeling and real-time response. This paper explores the development and evaluation of AI models specifically tailored to predict post-vaccine reactions based on individual health data, demographic features, vaccine type, and immunological profiles. Leveraging supervised machine learning algorithms and natural language processing (NLP) for electronic health record (EHR) mining, the study demonstrates a scalable solution for early detection and classification of side effects.