Hate speech detection in Bangla is challenging due to complex morphology, frequent code mixing, and severe class imbalance across categories such as abuse, sexism, religious and political hate, profanity, and neutrality. The BLP Workshop 2025 Subtask 1A addressed this by classifying Bangla YouTube comments into these categories to support online moderation in low-resource settings. We developed a BanglaBERT-based system with balanced data augmentation and advanced regularization techniques, combined with optimized training strategies for better generalization. On the blind test set, our system achieved a micro F1 score of 0.7013, ranking 21st on the leaderboard. These results indicate that augmentation, robust loss functions, and model refinements can enhance Bangla hate speech detection, though implicit and context-dependent hate speech remains difficult.