  This work concerns the nonlinear filtering problem of multiscale
McKean-Vlasov stochastic systems where the whole systems depend on
distributions of fast components. First of all, we prove that the slow
component of the original system converges to an average system in the $L^{2p}$
($p\geqslant 1$) sense. Moreover, we obtain the strong convergence order for
the $L^2$ case. Then, given an observation process which depends on the slow
component and its distribution, we show that the nonlinear filtering of the
slow component and its distribution also converges to that of the average
system in the $L^{q}$ ($p\geq 8, 1\leq q\leq \frac{p}{8}$) sense.
