You're right, there is lots of confusion in the topic and not all texts on regression get it right. Theoretically, linear regression depends only on a normally distributed outcome variable, the predictors don't have to be normally distributed. Practically speaking even that requirement can mostly be ignored: OLS is fairly insensitive to non-normal outcomes. Violations of the assumptions of regression usually don't affect the estimates much, they mainly affect the uncertainty around those parameter estimates -- the standard errors. This too is easily solved: use bootstrapping to calculate the standard errors.

In short: you can't just throw linear regression at anything and expect to get reasonable results, but it's pretty damn close.