Lipid nanoparticles are the leading nonviral platform for mRNA delivery, but their formulation requires coordinated optimization of lipid composition, microfluidic mixing conditions, RNA encapsulation, particle quality, and storage stability. This creates a high-dimensional development problem that is difficult to solve through static experimentation alone. Traditional formulation development often depends on sequential design-of-experiments workflows that do not adaptively learn from each new batch. As a result, stability behavior, manufacturability, and delivery performance may be evaluated too late in development, when reformulation becomes costly. This manuscript proposes a closed-loop artificial intelligence platform that links automated microfluidic LNP preparation with Bayesian optimization. The platform would iteratively select the next formulation experiment based on predicted size, polydispersity, encapsulation efficiency, and stability behavior. The conceptual platform includes a microfluidic synthesis robot, integrated particle characterization modules, stability measurement workflows, a Gaussian process surrogate model, an acquisition function optimizer, and a supervisory controller. Together, these components would allow autonomous experiment selection, execution, measurement, and model updating. Such a platform could improve formulation efficiency by learning from each experimental cycle and navigating trade-offs among competing critical quality attributes. It would also create a reusable knowledge base linking formulation variables, process fingerprints, product quality, and stability outcomes. Closed-loop AI could transform RNA nanomedicine formulation from a largely manual screening process into an adaptive, data-driven development system. This framework provides a conceptual foundation for autonomous LNP optimization without claiming experimental validation or numerical performance outcomes.