The widespread use of the internet has made sharing information on social media more convenient. At the same time, it provides a platform for individuals with malicious intent to easily spread hateful content. Since many users prefer to communicate in their native language, detecting hate speech in Bengali poses a significant challenge. This study aims to identify Bengali hate speech on social media platforms. A shared task on Bengali hate speech detection was organized by the Second Bangla Language Processing Workshop (BLP). To tackle this task, we implemented five traditional machine learning models (LR, SVM, RF, NB, XGB), three deep learning models (CNN, BiLSTM, CNN+BiLSTM), and three transformer-based models (Bangla-BERT, m-BERT, XLM-R). Among all models, a weighted ensemble of transformer models achieved the best performance.Our approach ranked 3rd in Subtask 1A with a micro-F1 score of 0.734, 6th in Subtask 1B with 0.7315, and, after post-competition experiments, 4th in Subtask 1C with 0.735.