Why we prefer VIF if we can find multicollinearity from correlation matrix as well? Why we prefer VIF if we can find multicollinearity from the correlation matrix as well? What is the exact logic behind it?
The correlation matrix is not a reliable measurement for multicollinearity  because it only considers the pairwise effects. Unfortunately, multicollinearity is defined as:


  Phenomenon in which two or more predictor variables in a multiple regression model are highly correlated,


Do you see the point? You'll need to consider the correlation with all other variables in your data set, not just 1-to-1 pairwise comparison.

VIF addresses the issue.