Detecting hate speech on social media is essential for safeguarding online communities, yet it remains challenging for low-resource languages like Bangla due to class imbalance and subjective annotations. We introduce a two-stage cascaded framework with k-fold ensembling to address the BLP Workshop 2025 Shared Task’s three subtasks: 1A (hate type classification), 1B (target identification), and 1C (joint classification of type, target, and severity). Our solution balances precision and recall, achieving micro-F1 scores of 0.7331 on 1A, 0.7356 on 1B, and 0.7392 on 1C, ranking 4th on 1A and 1st on both 1B and 1C. It performs strongly on major classes, although underrepresented labels such as sexism and mild severity remain challenging. Our method makes the optimal use of limited data through k-fold ensembling and delivers overall balanced performance across majority and minority classes by mitigating class imbalance via cascaded layers.