Fake profiles threaten privacy, security, and confidence as social media grows. This method uses textual, visual, and user behavior data to identify bogus profiles. This research develops a multimodal detection approach to increase the accuracy, precision, and recall of fraudulent profile identification. Multimodal feature extraction, fusion, and classification improve. Text, photos, and conduct reveal fake profiles. This holistic technique finds bogus profile signals. False profile causes and social media malice evolution are discussed. Multimodal testing is done. Benchmark datasets evaluate the framework’s accuracy, precision, recall, and F1-score to single-modal methods. Multimodal accuracy, precision, and recall are 93.5, 92.9, and 93.4%. The proposed strategy outperforms single-modality false profile detection. The study assesses the framework’s applicability across social media and demographics. User behaviors and platform features can identify fake profiles. Social media users and platforms trust this function more. This multimodal investigation finds fraudulent social media profiles. Text, graphics, and user activity assist in identifying fake profiles. The study finds and explains harmful phony profiles. The new structure improves social media security and reliability.