Fake news detection is critical in spreading misinformation on digital platforms. The Traditional methods were not able to capture complex relations and within propagated news on digital media networks. The Robust detection methods are required due to the severe concern raised by the rapid spread of bogus news on social media. Although they have shown potential, traditional graph-based neural networks find it difficult to simulate the intricate relationships found in social networks. Using the UPFD (User Profile false News identification) dataset, this paper provides a novel Deep Hypergraph Neural Network (HGNN) model for false news identification in this paper. The proposed model uses Hypergraphs to capture higher-order links between news articles, users, and their interactions, that addresses issues for managing multi-relational data in traditional. This allows for more subtle feature extraction and provides the HGNN in mathematical approach, with a Laplacian operator to propagate features and an incidence matrix. Our method achieves superior accuracy, precision, and F1-score than state-of-the-art techniques, as demonstrated by comparative studies.