The answer for your question is, yes.
By removing highly correlated predictors from the model multicollinearity can be prevented. If there are two or more factors with a high VIF, then remove one from the model because they supply redundant information, removing one of the correlated factors usually doesn't reduce the R-squared.
Or you can also use Partial Least Squares Regression (PLS) or Principal Components Analysis, regression methods that cut the number of predictors to a smaller set of uncorrelated components.
If you want to know more about Partial least Squares Regression then visit this Python Course.