The fast-growing number of content in Tamil in social media has led to increasing abusive and gender-directed hate speech in online platforms. Detecting abusive content written in Tamil is relatively difficult owing to the complex morphological structure of Tamil language, its dialects, transliteration, and contextualized usage. In this study, the use of transformer-based pretrained language models in detecting abusive content in Tamil was explored. Five transformer-based models—mBERT, MuRIL, XLM-RoBERTa, IndicBERT, and Tamil-BERT—were fine-tuned and tested using DravidianLangTech 2026 shared task dataset. The experimental results show that the best-performing model was Tamil-BERT with an accuracy rate of 80.72% owing to Tamil-specific pretraining and better morphological analysis capabilities. Our system ranks 5th at the leaderboard of the DravidianLangTech 2026 shared task challenge. The source code and fine-tuned models are opensource and publicly accessible.