Fokker--Planck (FP) equations describe the evolution of probability densities under stochastic dynamics, while Mean-Field Games (MFG) couple such a forward (in time) equation with a backward Hamilton--Jacobi--Bellman equation to model the equilibrium behaviour of large populations of interacting agents. Both arise across statistical physics, biology, economics, and machine learning, and both are closely tied to the geometry of optimal transport and Wasserstein gradient flows. However, their numerical solution is hindered by the curse of dimensionality, so that traditional grid-based discretizations quickly become intractable. Structured methods such as low-rank tensor formats, sparse grids, neural networks, and particle approximations can exploit the underlying structure, making high-dimensional problems of practical interest computationally feasible. This mini-symposium aims to bring together researchers working on high-dimensional approximation for FP equations and MFGs. Topics include low-rank tensor and sparse-grid methods, neural-network and particle approximations, model-order reduction, and optimal-transport and gradient-flow schemes, alongside the numerical analysis that supports them. A further goal is to connect the FP and MFG communities, which are mathematically closely related but often work separately.