High-dimensional time-series anomaly detection remains challenging due to the limitation of dimensionality, complex spatiotemporal dependencies, and heterogeneous multimodal feature interactions. To address these limitations, we propose a novel framework called the Multimodal Hypergraph Generative Adversarial Network (MHG-GAN). This framework leverages a multimodal generator to extract temporal and spatial features from raw time-series data, while a dynamic hypergraph structure explicitly models higher-order correlations among data points, overcoming the pairwise relationship constraints of traditional graph models. A dual-network adversarial architecture, comprising time-series and frequency-series generators, is developed to enhance data learning. In the training phase, time-series data is processed through a multimodal builder and transformed into time-domain and frequency-domain data. These data are then encoded by time-domain and frequency-domain encoders and fed into the respective generators. The generated data are constructed into hypergraphs, which are fused and input into a discriminator for judgment, alongside the original time-series data. In the detection phase, the process is similar, with the final score derived from reconstruction and discriminator judgment scores. Extensive experiments on real-world datasets, including SWaT for industrial control systems and NSL-KDD for network traffic monitoring, demonstrate that MHG-GAN outperforms state-of-the-art baselines (LSTM-VAE, MAD-GAN) in terms of F1-score, precision, accuracy, and recall metrics. Specifically, MHG-GAN achieves an F1-score of 95.1% on SWaT and 90.2% on NSL-KDD, significantly surpassing the performance of baseline models. This work advances high-dimensional temporal anomaly detection by integrating multimodal learning with hypergraph-structured adversarial optimization.