The third data release of Gaia has provided approximately 220 million low resolution spectra. Among these, about 100,000 correspond to white dwarfs. The magnitude of this quantity of data precludes the possibility of performing spectral analysis and type determination by human inspection. In order to tackle this issue, we explore the possibility of utilising a machine learning approach, based on a Random Forest algorithm. We aim to analyze the viability of the Random Forest algorithm for the spectral classification of the white dwarf population within 100 pc from the Sun, based on the Hermite coefficients of Gaia spectra. We utilized the assigned spectral type from the Montreal White Dwarf Database for training and testing our Random Forest algorithm. Once validated, our algorithm model is applied to the rest of unclassified white dwarfs within 100 pc. First, we started by classifying the two major spectral type groups of white dwarfs: hydrogen-rich (DA) and hydrogen-deficient (non-DA). Next, we explored the possibility of classifying the various spectral subtypes, including in some cases the secondary spectral types. Our Random Forest classification presented a very high recall (>80%) for DA and DB white dwarfs, and a very high precision (>90%) for DB, DQ and DZ white dwarfs. As a result we have assigned a spectral type to 9,446 previously unclassified white dwarfs: 4,739 DAs, 76 DBs (60 of them DBAs), 4,437 DCs, 132 DZs and 62 DQs (9 of them DQpec). Despite the low resolution of Gaia spectra, the Random Forest algorithm applied to the Gaia spectral coefficients proves to be a highly valuable tool for spectral classification.