We study the maximum likelihood estimation (MLE) in the multivariate deviated model where the data are generated from the density function $(1-λ^{\ast})h_{0}(x)+λ^{\ast}f(x|μ^{\ast}, Σ^{\ast})$ in which $h_{0}$ is a known function, $λ^{\ast} \in [0,1]$ and $(μ^{\ast}, Σ^{\ast})$ are unknown parameters to estimate. The main challenges in deriving the convergence rate of the MLE mainly come from two issues: (1) The interaction between the function $h_{0}$ and the density function $f$; (2) The deviated proportion $λ^{\ast}$ can go to the extreme points of $[0,1]$ as the sample size tends to infinity. To address these challenges, we develop the \emph{distinguishability condition} to capture the linear independent relation between the function $h_{0}$ and the density function $f$. We then provide comprehensive convergence rates of the MLE via the vanishing rate of $λ^{\ast}$ to zero as well as the distinguishability of two functions $h_{0}$ and $f$.