Messenger RNA (mRNA) therapeutics have emerged as a transformative platform for treating cancer and many other diseases. However, their clinical success critically depends on the development of lipid nanoparticles (LNPs) capable of selectively delivering mRNA to target cell populations while overcoming intracellular delivery barriers. This presentation will showcase our recent advances in engineering precision mRNA LNPs through the integration of artificial intelligence (AI), high-throughput screening platforms, and nanomedicine. First, I will present an mRNA LNP platform that selectively targets tumour-associated macrophages using an antibody conjugation strategy to remodel the immunosuppressive tumour microenvironment and enhance cytotoxic T lymphocyte-mediated anti-tumour immunity. Next, I will introduce a small-sample AI platform that rapidly identifies functional ionisable lipids from limited experimental data, enabling efficient mRNA delivery to cancer-associated fibroblasts. I will further demonstrate how transfer learning extends this approach to accelerate lipid discovery for dendritic cell-targeted mRNA delivery. Finally, I will present a new generation of LNPs engineered to significantly enhance endosomal escape, achieving highly efficient mRNA transfection in primary T cells and providing a promising non-viral strategy for CAR mRNA engineering. Furthermore, leveraging our high-throughput lipid synthesis capability and automated mRNA LNP formulation using our microfluidic platforms, we are advancing towards an integrated, automated, high-throughput, and AI-guided platform for mRNA-LNP therapeutics discovery. Our work lays the foundation for next-generation precision cancer immunotherapies and demonstrates how AI-guided nanomedicine can accelerate the development of targeted mRNA therapeutics.