Rising deepfakes and false information on social media constitute a major danger to digital security, democracy, and public trust. Conventional text-based detection techniques fall short of solving the rising sophistication of altered material including images, video, and sound. By using natural language processing (NLP), computer vision, and audio analysis in a multimodal analysis framework, this study aims to detect and categorize deepfake material and connected misinformation. The system exceeds unimodal solutions in accuracy and robustness by combining visual cues (e.g., facial inconsistencies), acoustic patterns (e.g., voice synthesis artifacts), and textual analysis (e.g., fact-checking and sentiment). Integrated processing of data from sites like Twitter, Facebook, and YouTube combines deep learning and machine learning models-CNNs for image/video analysis and transformers for text understanding. Across several datasets, experimental findings show that the suggested model surpasses basic systems in both recall and accuracy. This study emphasizes how crucial multimodal approaches are for preserving information integrity in the digital age.
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