Pandemic era warnings about the use of deepfake technology, which uses artificial intelligence to create convincing audio and video content, had raised legitimate fears of privacy, security and misinformation in the early years of the decade. For this purpose, we are going to construct a short and simple version of the project called “Video and Voice-Based Deep Fake” in which a strong multimodal detection mechanism will be recommended that may even be used to verify inconsistency of every video audio and visual they are manipulated. CNNs (Convolutional Neural Networks) are employed for visual inspection for every successive frame to spot slight differences while RNNs or transformer-based models like BERT and RoBERTA are utilized for temporal representation for audio signals. In terms of pipeline for deepfake detection, it consists of data preprocessing, followed by feature extraction, hyper unified multimodal integration and finally classification, resulting in high level of accuracy in detection of deepfake content. Based on multi-modal datasets, the system scales and can score for accuracy a fingerprint and variations of it up to real-time, with potential applications for digital forensics, media verification and public safety. Using both video and audio mechanisms, this project implements a system that increases the reliability and accuracy of deepfake detection for multiple media formats.