We present our system for the DravidianLangTech 2026 shared task on multi-level political meme classification in Tamil and Malayalam. The task involves two hierarchical levels: (1) stance detection (Support vs. Troll) and (2) target identification (Person, Party, or Intersection). Our approach combines CLIP vision-language embeddings (ViT-L-14) with face detection features and political logo similarity matching, resulting in a 773-dimensional feature representation. We train separate LinearSVC classifiers for each language and task level. Our system achieved Rank 1 in Malayalam with an average F1-score of 0.7930 and Rank 6 in Tamil with 0.7666. Our codes are available at https://github.com/A-k-a-sh/Shared-task-multimodal-political-meme.