Hate speech detection in low-resource languages such as Tamil presents significant challenges due to linguistic complexity, limited annotated data, and the sociocultural sensitivity of the subject matter. This study focuses on identifying caste- and migration-related hate speech in Tamil social media texts, as part of the LT-EDI@LDK 2025 Shared Task. The dataset used consists of 5,512 training instances and 787 development instances, annotated for binary classification into caste/migration-related and non-caste/migration-related hate speech. We employ a range of models, including Support Vector Machines (SVM), Convolutional Neural Networks (CNN), and transformer-based architectures such as BERT and multilingual BERT (mBERT). A central focus of this work is evaluating model performance using macro F1-score, which provides a balanced assessment across this imbalanced dataset. Experimental results demonstrate that transformer-based models, particularly mBERT, significantly outperform traditional approaches by effectively capturing the contextual and implicit nature of hate speech. This research underscores the importance of culturally informed NLP solutions for fostering safer online environments in underrepresented linguistic communities such as Tamil.