This study introduces a comprehensive deep learning framework for the detection, classification, segmentation, and tracking of solar filaments using H-Alpha images from the Global Oscillation Network Group (GONG) data archive. Solar filaments, phenomena in the solar corona, are of significant scientific interest due to their link with violent eruptive events such as coronal mass ejections. Using together a DETR-based model for detection and classification, a U-Net for instance segmentation, and a custom-made tracking algorithm, we achieved state of the art performance across all tasks, overcoming typical challenges. The proposed methodology significantly advances solar filament analysis, offering improved capabilities for automated studies and potential applications in space weather prediction.