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Abstract Nucleic acid nanoparticles (NANPs) represent a versatile platform for drug delivery and modulation of therapeutic responses. To expedite NANPs’ translation from bench to bedside, rapid coordination of their design principles with immunostimulatory assessment is essential. Here, a deep learning framework is presented to predict cytokine responses, specifically interferon‐beta (IFN‐β) and interleukin‐6 (IL‐6), induced by NANPs in human microglial cells based solely on their sequences. Using a transformer‐based architecture augmented through systematic strand permutation trained on 176 structurally diverse, individually assembled, and experimentally characterized NANPs, the model achieved high predictive performance in cross‐validation (R²= 0.96–0.97, RMSE ≤ 0.01) and demonstrated strong generalizability on an external test set (R²= 0.91 for IFN‐β; 0.85 for IL‐6). This work advances sequence‐based quantitative structure‐activity relationship (QSAR) modeling by leveraging attention‐based neural networks to eliminate the need for manual feature engineering while maintaining biological interpretability. To facilitate community access, the updated artificial immune cell (AI‐cell) web‐based platform is introduced, which supports rapid immune profiling of NANPsin silico. This new approach methodology provides a scalable framework to guide the rational design and optimization of NANPs through rapid prediction of their immune responses.