Abusive language targeting women on Tamil social media is a growing concern that necessitates automated detection systems capable of handling low-resource, code-mixed, and morphologically rich text. This paper presents the SUPERNOVA system submitted to the shared task on Abusive Tamil Text Targeting Women on Social Media at DravidianLangTech@ACL 2026. We investigate three complementary approaches: (1) fine-tuning MuRIL with class balancing and label smoothing, (2) MuRIL contextual embeddings combined with XG-Boost and decision threshold tuning, and (3) a lightweight ensemble of character-level TF-IDF and SentenceBERT features with Random Forest and Extra Trees. Our best system achieves an accuracy of 0.8007 and a macro F1-score of 0.7994, ranking 11th among all participating teams. These results highlight the effectiveness of multilingual transformer representations combined with ensemble techniques for the detection of abusive text on Tamil social networks. The code is publicly available at https://github.com/Kiruthi001/SuperNova-DravidianLangTech-ACL2026.