Detecting hate speech in Bangla is challenging due to its complex vocabulary, spelling variations, and region-specific word usage. However, effective detection is essential to ensure safer social media spaces and to take appropriate action against perpetrators. In this study, we report our participation in Subtask A of Task 1: Bangla Hate Speech Detection (Hasan et al., 2025b). In addition to the provided 50K Bangla comments (Hasan et al., 2025a), we collected approximately 4K Bangla comments and employed several data augmentation techniques. We evaluated several transformer-based models (e.g., BanglaBERT, BanglaT5, BanglaHateBERT), achieving the best performance with a micro-F1 score of 71% and securing 18th place in the Evaluation Phase.