The DravidianLangTech@NAACL 2025 shared task focused on multimodal hate speech detection in Tamil, Telugu, and Malayalam using social media text and audio. Our approach integrated advanced preprocessing, feature extraction, and deep learning models. For text, preprocessing steps included normalization, tokenization, stopword removal, and data augmentation. Feature extraction was performed using TF-IDF, Count Vectorizer, BERT-base-multilingual-cased, XLM-Roberta-Base, and XLM-Roberta-Large, with the latter achieving the best performance. The models attained training accuracies of 83% (Tamil), 88% (Telugu), and 85% (Malayalam). For audio, Mel Frequency Cepstral Coefficients (MFCCs) were extracted and enhanced with augmentation techniques such as noise addition, time-stretching, and pitch-shifting. A CNN-based model achieved training accuracies of 88% (Tamil), 88% (Telugu), and 93% (Malayalam). Macro F1 scores ranked Tamil 3rd (0.6438), Telugu 15th (0.1559), and Malayalam 12th (0.3016). Our study highlights the effectiveness of text-audio fusion in hate speech detection and underscores the importance of preprocessing, multimodal techniques, and feature augmentation in addressing hate speech on social media.