Proverbs carry wisdom transferred orally from generation to generation. Based on the place they were recorded, this study organised more than one hundred thousand Greek proverbs into a machine-actionable dataset, which we analysed using linguistic distance as our lens. By focusing on the most widespread proverbs, our analysis showed that proverbs transferred not only over time but also spatially, existing exactly the same in different, far away locations. In order to study the travels of proverbs, we devised an algorithm based on the shortest linguistic path and the reverse triangular inequality, which we applied to the most widespread proverb, discussing our findings. By focusing on the least widespread proverbs, we benchmarked geographical attribution, using text classification and text geocoding. Our classification results showed that although this is a challenging task (33% in macro-averaged F1), proverbs of specific areas can be attributed with more than 80% F1. They also showed that in this task, transformer-based transfer learning can be outperformed by standard machine learning on top of frequency-based features. The same finding stands for the relatively easier latitudinal prediction in proverb geocoding. Finally, by using important features as a means, we presented words moving our regression predictions toward the east, west, south, and north.