This paper presents an in-depth study on multimodal hate speech detection in Dravidian languages—Tamil, Telugu, and Malayalam—by leveraging both audio and text modalities. Detecting hate speech in these languages is particularly challenging due to factors such as codemixing, limited linguistic resources, and diverse cultural contexts. Our approach integrates advanced techniques for audio feature extraction and XLM-Roberta for text representation, with feature alignment and fusion to develop a robust multimodal framework. The dataset is carefully categorized into labeled classes: gender-based, political, religious, and personal defamation hate speech, along with a non-hate category. Experimental results indicate that our model achieves a macro F1-score of 0.76 and an accuracy of approximately 85.