One major hurdle we faced while developing our AI-powered content moderation system was efficiently analyzing multimodal content-where text and images needed to be processed together for accurate classification. Initially, analyzing them separately led to misclassifications, such as flagging an image as harmful while its accompanying text was harmless. To solve this, we implemented a multimodal AI model that processes text and images simultaneously using BERT for NLP and ResNet for computer vision, along with a cross-modal attention mechanism to improve contextual understanding. Additionally, we introduced an adaptive confidence scoring system, ensuring uncertain cases were reviewed by humans, which significantly reduced false positives and improved accuracy while maintaining real-time performance.