This study applies machine learning (ML) techniques to classify aerosols using high-resolution multiwavelength lidar data from EARLINET network. We developed a reference dataset and evaluated six ML models, with LightGBM achieving over 90 % accuracy. Depolarization data proved critical for improving dust classification. Validated against independent datasets, our approach improves aerosol classification and may help refine lidar-based processing strategies.