The rapid growth of digital communication and social media platforms has transformed the way information is shared and consumed globally. However, the increasing spread of misinformation, fake news, manipulated media, and misleading online content has created serious social, political, and economic challenges. Traditional misinformation detection systems often fail to understand contextual meaning, emotional manipulation, and inconsistencies between text and visual content. This research paper proposes TruthGuard, an AI-powered multimodal misinformation detection framework that integrates Natural Language Processing (NLP), Machine Learning, Deep Learning, and image verification techniques. The proposed system uses Transformer-based architectures such as BERT along with CNN and ResNet models for image analysis. The framework also introduces explainable AI mechanisms to improve transparency and user trust. Experimental observations indicate that multimodal hybrid systems achieve significantly higher accuracy and better contextual understanding than traditional machine learning approaches. The proposed framework aims to create safer digital communication environments and reduce the harmful effects of online misinformation.