Social media is a powerful communication tooland rich in diverse content requiring innovativeapproaches to understand nuances of the lan-guages. Addressing challenges like hate speechnecessitates multimodal analysis that integratestextual, and other cues to capture its contextand intent effectively. This paper proposes amultimodal hate speech detection system inTamil, which uses textual and audio featuresfor classification. Our proposed system usesa fine-tuned Indic-BERT model for text basedhate speech detection and Wav2Vec2 modelfor audio based hate speech detection of au-dio data. The fine-tuned Indic-BERT modelwith Whisper achieved an F1 score of 0.25 onMultimodal approach. Our proposed approachranked at 10th position in the shared task onMultimodal Hate Speech Detection in Dravid-ian languages at the NAACL 2025 WorkshopDravidianLangTech.