Identifying new white dwarfs (WDs) heavy elements is crucial, as they serve as valuable tools for deducing the chemical characteristics of potential planetary systems accreting material onto their surfaces. To detect metallic WDs, we propose a methodology based on an unsupervised learning technique known as Self-Organizing Maps (SOM). This approach projects a high-dimensional dataset onto a two-dimensional grid, where similar elements are grouped into the same neuron. Using this method, we uncovered 143 bona fide WD candidates in the Gaia space mission with several metallic lines in their spectra, including Ca, Mg, Na, Li, and K. The precision metrics achieved with our method are comparable to those of recent supervised techniques.