The rise in utilizing social media platforms increased user-generated content significantly, including negative comments about women in Tamil and Malayalam. While these platforms encourage communication and engagement, they also become a medium for the spread of abusive language, which poses challenges to maintaining a safe online environment for women. Prevention of usage of abusive content against women as much as possible is the main issue focused in the research. This research focuses on detecting abusive language against women in Tamil and Malayalam social media comments using computational models, such as Logistic regression model, Support vector machines (SVM) model, Random forest model, multilingual BERT model, XLM-Roberta model, and IndicBERT. These models were trained and tested on a specifically curated dataset containing labeled comments in both languages. Among all the approaches, IndicBERT achieved a highest macro F1-score of 0.75. The findings emphasize the significance of employing a combination of traditional and advanced computational techniques to address challenges in Abusive Content Detection (ACD) specific to regional languages.