This study presents the development of an automated aerosol typing model utilizing Mie-Raman-fluorescence lidar data collected by LILAS, located on the ATOLL platform in Lille, France. The proposed model, FLARE-GMM, employs a Gaussian Mixture Model trained on a dataset spanning from early 2021 to the end of 2023. FLARE-GMM is able to distinguish between dust, urban and biomass burning aerosols by using the 𝑃𝐿𝐷𝑅 and the fluorescence capacity as well as RH, all measured with LILAS. To ensure accurate model training, cases were manually selected to include only pure aerosol layers, as mixed aerosols are not accurately modelled by GMM. Following the training phase, the model's performance was evaluated by investigating extreme events in which the aerosol type is not ambiguous. This approach was also completed with the use of a test dataset on which FLARE-GMM was compared to NATALI, another automatic aerosol typing model based on neural networks using lidar data. The results demonstrated that FLARE-GMM shows promise in accurately identifying aerosol types, indicating its potential for classifying aerosols in a variety of situations. Finally, FLARE-GMM was used to estimate the aerosol types present in Lille's atmosphere throughout the entire dataset from early 2021 to the end of 2023. A statistical analysis of these results was conducted, further underscoring the model's capability in automated aerosol classification.