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The in-plane thermal conductivity of hexagonal boron nitride (h-BN) with varying thicknesses is a key property that affects the performance of various applications from electronics to optoelectronics. However, the transition of the thermal conductivity from two-dimensional (2D) to three-dimensional (3D) h-BN remains elusive. To answer this question, we have developed a machine learning interatomic potential within the neuroevolution potential (NEP) framework for h-BN, achieving a high accuracy akin to ab initio calculations in predicting its thermal conductivity and phonon transport from monolayer to multilayers and bulk. Utilizing molecular dynamics simulations based on the NEP, we predict the thermal conductivity of h-BN with a thickness up to ∼100 nm, demonstrating that its thermal conductivity quickly decreases from the monolayer and saturates to the bulk value above four layers. The saturation of its thermal conductivity is attributed to the little change in phonon group velocity and lifetime as the thickness increases beyond four layers. In particular, the weak thickness dependence of phonon lifetime in h-BN with a nanoscale thickness results from its extremely high phonon focusing along the in-plane direction. This research bridges the knowledge gap of phonon transport between 2D and 3D h-BN and will benefit the thermal design and performance optimization of relevant applications. [ABSTRACT FROM AUTHOR]