Mahalanobis vs Normalization+Euclidean
Both are reasonable approaches and it is foreseeable that either one could outperform the other empirically. The Euclidean distance assumes the data to be isotropically Gaussian, i.e. it will treat each feature equally. On the other hand, the Mahalanobis distance seeks to measure the correlation between variables and relaxes the assumption of the Euclidean distance, assuming instead an anisotropic Gaussian distribution.

If you know a priori that there is some kind of correlation between your features, then I would suggest using a Mahalanobis distance over Euclidean. Also, note that Z-score feature scaling can mitigate the usefulness of choosing a Mahalanobis distance over Euclidean (less true of min-max normalization though). The major drawback of the Mahalanobis distance is that it requires the inversion of the covariance matrix which can be computationally restrictive depending on the problem.