The convergence of artificial intelligence (AI) and nanomedicine has transformed cancer vaccine development, particularly in optimizing RNA-loaded lipid nanoparticles (LNPs). Stability and targeted delivery are major obstacles to the clinical translation of promising RNA-LNP vaccines for cancer immunotherapy. This systematic review analyzes the AI’s impact on LNP engineering through machine learning-driven predictive models, generative adversarial networks (GANs) for novel lipid design, and neural network-enhanced biodistribution prediction. AI reduces the therapeutic development timeline through accelerated virtual screening of millions of lipid combinations, compared to conventional high-throughput screening. Furthermore, AI-optimized LNPs demonstrate improved tumor targeting. GAN-generated lipids show structural novelty while maintaining higher encapsulation efficiency; graph neural networks predict RNA-LNP binding affinity with high accuracy vs. experimental data; digital twins reduce lyophilization optimization from years to months; and federated learning models enable multi-institutional data sharing. We propose a framework to address key technical challenges: training data quality (min. 15,000 lipid structures), model interpretability (SHAP > 0.65), and regulatory compliance (21CFR Part 11). AI integration reduces manufacturing costs and makes personalized cancer vaccine affordable. Future directions need to prioritize quantum machine learning for stability prediction and edge computing for real-time formulation modifications.

Artificial intelligence is reshaping RNA research by enabling predictive, data-driven workflows across molecular design, therapeutic development, delivery optimization, and analytical quality control. RNA-based medicines, including messenger RNA vaccines, small interfering RNA therapeutics, antisense oligonucleotides, RNA-guided systems, and RNA-targeted small molecules, are programmable but structurally and analytically complex. Their performance depends not only on nucleotide sequence, but also on RNA folding, untranslated regions, chemical modifications, formulation composition, delivery efficiency, stability, manufacturability, and critical quality attributes. This review examines how AI is applied across the RNA therapeutic development pipeline, including RNA sequence and structure prediction, codon optimization, guide RNA design, neoantigen selection, RNA-targeted drug discovery, lipid nanoparticle formulation prediction, and analytical data integration. Particular emphasis is placed on AI-enabled analytics as a translational layer between computational design and therapeutic implementation. Centralized multimodal data platforms that integrate outputs from orthogonal analytical methods can transform fragmented experimental readouts into standardized, model-ready datasets. We argue that future progress will depend on closed-loop systems in which design models, delivery prediction, experimental analytics, and quality-control data continuously inform each other to improve RNA therapeutic translation.