The identification of new white dwarfs (WDs) polluted with heavy elements is important since they provide a valuable tool for inferring the chemical properties of putative planetary systems accreting material on their surfaces. The Gaia space mission has provided us with an unprecedented amount of astrometric, photometric, and low-resolution (XP) spectroscopic data for millions of newly discovered stellar sources, among them thousands of WDs. In order to find WDs among these data and to identify which ones have metals in their atmospheres, we propose a methodology based on an unsupervised artificial intelligence technique called self-organizing maps. In our approach, a nonlinear high-dimensional data set is projected on a 2D grid map where similar elements fall into the same neuron. By applying this method, we obtained a clean sample of 66,337 WDs. We performed an automatic spectral classification analysis on them, obtaining 143 bona fide polluted WD candidates not previously classified in the literature. The majority of them are cool WDs and we identify in their XP spectra several metallic lines such as Ca, Mg, Na, Li, and K. The fact that we obtain similar precision metrics to those achieved with recent supervised techniques highlights the power of our unsupervised approach to mine the Gaia archives for hidden treasures to follow up spectroscopically with higher resolution.