In recent years, the introduction of mRNA vaccines for SARS-CoV2 and RSV has highlighted the success of the mRNA technology platform. Designing mRNA sequences involves multiple components and requires balancing several parameters, including enhancing transcriptional efficiency, boosting antigenicity, and minimizing immunogenicity. Moreover, changes in the composition and properties of delivery vehicles can also affect vaccine performance. Traditional methods of experimentally testing these conditions are time-consuming, labor-intensive and costly, necessitating advanced optimization strategies. Recently, the rapid development of computational tools has significantly accelerated the optimization process for mRNA vaccines. In this review, we systematically examine computation-aided approaches for optimizing mRNA components, including coding and non-coding regions, and for improving the efficiency of lipid nanoparticle (LNP) delivery systems by focusing on their composition, ratios, and characterization. The use of computational tools can significantly accelerate mRNA vaccine development, enabling rapid responses to emerging infectious diseases and supporting the development of precise, personalized therapies. These approaches may guide the future direction of mRNA vaccine development. Our review aims to provide integrated constructive support for computer-aided mRNA vaccine design.