Similar to correlation, linear regression is an approach to model the relationship between a normally distributed interval outcome variable y and one or more predictor variables denoted X. The case of one predictor variable is called simple linear regression. For more than one predictor variable, it is called multiple linear regression. (This term should be distinguished from multivariate linear regression).
The difference is that correlation does not have ‘direction’ while regression has (one dependent variable and one independent variable)