It is difficult to detect hate speech in codemixed Dravidian languages because the data is multilingual and unstructured. We took part in the shared task to detect hate speech in text and audio data for Tamil, Malayalam, and Telugu in this research. We tested different machine learning and deep learning models such as Logistic Regression, Ridge Classifier, Random Forest, and CNN. For Tamil, Logistic Regression gave the best macro-F1 score of 0.97 for text, whereas Ridge Classifier was the best for audio with a score of 0.75. For Malayalam, Random Forest gave the best F1-score of 0.97 for text, and CNN was the best for audio (F1 score: 0.69). For Telugu, Ridge Classifier gave the best F1-score of 0.89 for text, whereas CNN was the best for audio (F1-score: 0.87).Our findings prove that a multimodal solution effi ciently tackles the intricacy of hate speech detection in Dravidian languages. In this shared task,out of 145 teams we attained the 12th rank for Tamil and 7th rank for Malayalam and Telugu.