Machine learning (ML) is revolutionising the analysis of large astronomical surveys such as Gaia DR3. We present a two-step ML pipeline to classify Asymptotic Giant Branch (AGB) stars: (i) cosine similarity identifies candidate AGB stars from 140 million sources, and (ii) an XGBoost classifier which separates carbon-rich (C-rich) and oxygen-rich (O-rich) stars. Our classifier achieves ROC-AUC > 99.9%, producing a catalogue of 307,191 O-rich and 4,227 C-rich stars. Feature importance analysis shows that the sixth RP coefficient (RP6) dominates classification, underlining the critical role of Gaia red spectral features. This work demonstrates how hybrid ML approaches can efficiently identify chemically distinct stellar populations in massive survey datasets, providing opportunities for new studies of Galactic structure and stellar evolution.

Classification algorithms based on neural network techniques are applied to study if the bulge stars can be differentiated from other stars belonging to other Galactic components. A synthetic sample is build as a mixture of four components, thin disk, thick disk, halo and bulge, according to some stellar system models and considering observational errors.