Linear regression doesn't require or assume the input data values to be normally distributed, just that the residuals are normally distributed and have constant variance.
Further, those assumptions are only necessary for confidence intervals and hypothesis tests.
Linear regression doesn't require much, honestly, just non-null numeric data. My point was more on the interpretations of the outputs.
The constant variance part is important though. If you apply linear regression to a severely heteroscedastic dataset, the inferences you obtain from the model will be incorrect.