why does making the target variable normally distributed helps?
In some cases, it may actually help getting better results (depending on the model type), but it is also likely that the improvement comes from the fact that the performance metric is computed differently. For instance, a skewed distribution will lead to high MSE values due to cases located on the other side of the distribution, while the MSE is limited if the data is transformed to a normal distribution. So when comparing the cases, make sure you evaluate the performance on the back-transformed target.

Cases where the model will actually perform better with a normally distributed target include, among others, Gaussian process regression, because of the underlying assumption of a Gaussian random variable. There should be quite a few other model types which somehow have similar assumptions, and thus perform better with transformed data.