Solid state ionic conductors are of primary importance for the design of tomorrow's batteries. In lithium ion or sodium ion-based materials, the alkali cations diffuse through three-dimensional channels consisting of interconnected tetrahedral or octahedral sites with low free energy barriers between them. Fluoride ion conductors stand out in this landscape since the materials with the highest conductivities belong to the MSnF₄ family (in which M²⁺ is a divalent cation), which structure is layered and characterized by double-layers of Sn²⁺ and M²⁺ cations along a given direction. Importantly, these materials display stereoactive electron lone pairs (LPs) that seemingly play an important role not only in stabilizing of the Sn-Sn layer but also in modulating the fluoride ion diffusive behavior. However, despite previous experimental and simulation studies, the involvement of the LPs in the fluoride ions conduction mechanism remains to be quantitatively understood. In this work, we simulate the BaSnF₄ tetragonal structure using machine learning-based molecular dynamics, in which the interaction potential is trained on density functional theory data. We investigate the role of the Sn-LP-Sn layer in lowering the diffusion energy landscape. In particular, we show how the F -ions jump across this layer occur much more frequently than in the Ba-F-Ba one, resulting in the formation of vacancies in the Ba-Sn layers. Concurrently, the LPs stereochemical activity fluctuates to accommodate the F ions jumping. In addition, the presence of the LP layer enhances the flexibility of the Sn ions, which leads to an increase of the two-dimensional diffusion by several orders of magnitude. These results contribute to the understanding of the interplay between LPs and ionic diffusion, helping to explain the good performance of the material in fluoride-ion batteries.