Ensuring a safe and inclusive online environment requires effective hate speech detection on social media. While detection systems have significantly advanced for English, many regional languages, including Malayalam, Tamil and Telugu, remain underrepresented, creating challenges in identifying harmful content accurately. These languages present unique challenges due to their complex grammar, diverse dialects, and frequent code-mixing with English. The rise of multimodal content, including text and audio, adds further complexity to detection tasks. The shared task “Multimodal Hate Speech Detection in Dravidian Languages: DravidianLangTech@NAACL 2025” has aimed to address these challenges. A Youtube-sourced dataset has been provided, labeled into five categories: Gender (G), Political (P), Religious (R), Personal Defamation (C) and Non-Hate (NH). In our approach, we have used mBERT, T5 for text and Wav2Vec2 and Whisper for audio. T5 has performed poorly compared to mBERT, which has achieved the highest F1 scores on the test dataset. For audio, Wav2Vec2 has been chosen over Whisper because it processes raw audio effectively using self-supervised learning. In the hate speech detection task, we have achieved a macro F1 score of 0.2005 for Malayalam, ranking 15th in this task, 0.1356 for Tamil and 0.1465 for Telugu, with both ranking 16th in this task.