Carbon capture and storage is a vital strategy for mitigating rising atmospheric carbon dioxide, and metal–organic frameworks (MOFs) have gained attention as promising sorbents. Numerous simulations have examined factors governing CO₂ capture in MOFs—such as diffusion in MOF-74 under varying temperatures and process modeling of MOF-5—but most were limited to specific structures or conditions, hindering a systematic understanding of diffusion across diverse MOFs. Conventional computational methods also face constraints: density functional theory mainly provides static energy evaluations, while molecular dynamics relies on fixed force fields with poor transferability and an inability to describe reactive events. To overcome these limitations, this study employs molecular dynamics simulations driven by neural network potentials to evaluate CO₂ diffusivity in 17 types of MOFs. Results reveal significant variation in transport behavior, with zeolitic-imidazolate framework-3 showing the highest diffusivity and MOF-74 the lowest—an approximately 19-fold difference. These findings highlight the capability of neural-network-based molecular dynamics to deliver consistent and quantitative assessments of CO₂ transport in MOFs, providing a reliable framework for the rational design of next-generation capture materials.