Deepfake technology which allows the generation of extremely realistic fake media has become a major threat evoking concern in society as well as with individuals. We propose a multimodal deepfake detection system that leverages common deep learning techniques on the audio, video, image, and textual modalities to improve deepfake detection accuracy. The architecture combines CNNs to comprehend image and video, Transformers and LSTMs to identify text deepfake, and other speech analysis tools based on deep learning models to compare the speech authenticity. This system provides a holistic approach to deepfake detection by combining these modalities. It also uses feature fusion and cross-modal learning to enhance detection performance and reduce false positives. This framework can be of great help in detecting real-time deepfake as it is quick and accurate, therefore it can be utilized in different applications to counter fake news and validate media. The detection system based on AI reinforces digital security with a robust, multimodal procedure for detecting and preventing deepfakes.