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Social media has effectively replaced traditional forms of communication and marketing. As these platforms allow for the free expression of ideas and facts through text, images, and videos, there exists a significant need to screen them to safeguard people and organisations from objectionable information directed at them. Our work aims to categorise code-mixed social media comments and posts in Tamil, Malayalam, and Kannada into offensive or not offensive at different levels. We present a multilingual MPNet and CNN fusion model for detecting offensive language content directed at an individual (or group) in low-resource Dravidian languages at different levels. Our model is capable of handling data that has been code-mixed, such as Tamil and Latin scripts. The model was successfully validated on the datasets, achieving offensive language detection results better than those of other baseline models with weighted average F1-score of 0.85, 0.98, and 0.76, and performed better than the baseline models EWDT, and EWODT by 0.02, 0.02, 0.04 for Tamil, Malayalam, and Kannada respectively.