Political memes are widely used to express opinions, sarcasm, and ideological narratives on social media platforms. However, detecting political trolling in low-resource languages such as Tamil and Malayalam remains challenging due to limited datasets and tools. To address this problem, DravidianLangTech@ACL 2026 organized a shared task on hierarchical political meme classification.This work explores text-only models, classical multimodal fusion, and Vision-Language Models (VLMs) for Tamil and Malayalam political meme classification. Our experiments include IndicBERTv2, XLM-RoBERTa, EfficientNet-based multimodal fusion, and Qwen-VL models. Among the submitted systems, Qwen2.5-VL-7B-Instruct with 4-bit QLoRA fine-tuning achieved competitive performance, ranking 3rd in the Malayalam track and 4th in the Tamil track based on weighted-F1 score. Additional post-evaluation experiments with Qwen3-VL-8B further improved macro-F1 performance, highlighting the effectiveness of VLMs for low-resource multilingual political meme classification.