We extend the concept of self-consistency for the Fokker-Planck equation (FPE) [Shen et al., 2022]
to the more general McKean-Vlasov equation (MVE). While FPE describes the macroscopic behavior of particles under drift and diffusion, MVE accounts for the additional inter-particle interactions,
which are often highly singular in physical systems. Two important examples considered in this paper are the MVE with Coulomb interactions and the vorticity formulation of the 2D Navier-Stokes
equation. We show that a generalized self-consistency potential controls the KL-divergence between
a hypothesis solution to the ground truth, through entropy dissipation. Built on this result, we propose to solve the MVEs by minimizing this potential function, while utilizing the neural networks
for function approximation. We validate the empirical performance of our approach by comparing
with state-of-the-art NN-based PDE solvers on several example problems.