Adverse drug reaction (ADR) severity prediction is a key task in pharmacovigilance and post-market drug safety surveillance, but is of- ten challenged by noisy and heterogeneous clinical data. We propose a retrieval-augmented learning framework that leverages historical FAERS reports to construct evidence-enriched training instances for ADR severity prediction. Each ADR query, including patient demographics, drug exposure, and indications, is encoded using a fine-tuned BGE-M3 embedding model. We retrieve the top five relevant cases using BM25 lexical search and the top five semantically similar cases using dense vector search, and fuse them via an attention-based aggregation mechanism to produce retrieval-aware representations. These representations are evaluated using machine learning, deep learning, transformer, and fine-tuned large language model approaches for binary severity prediction. Results show that a tree-based ensemble achieves the best performance with an AUC of 82.3%, outperforming the strongest deep learning baseline by 2.3 percentage points and fine-tuned LLMs by 17 percentage points. Overall, the findings demonstrate that retrieval-enhanced data preprocessing can improve representation quality for ADR severity prediction, while the proposed upstream data engineering approach enables classical ensemble methods to remain highly effective for large-scale pharmacovigilance risk prediction.