Nowadays, high-resolution hyperspectral data is acquired by advanced imaging sensors capable of capturing dense spectral information across hundreds of contiguous bands. This results in detailed reflectance profiles that provide rich spatial and spectral representations of the surface of the Earth, enabling a wide range of remote sensing applications such as land-cover classification, environmental monitoring, and resource management. However, the high spectral dimensionality of hyperspectral data, combined with the typically limited availability of labeled training samples, gives rise to the well-known curse of dimensionality. This phenomenon leads to increased model complexity, risk of overfitting, and limited generalization in supervised learning scenarios. In response to these challenges, recent research has focused on generative models as a promising class of approaches for hyperspectral image processing. These models support unsupervised representation learning, realistic spectral reconstruction, and data augmentation, thereby enhancing model robustness and performance in data-scarce and high-dimensional settings. This chapter explores the role of generative models in addressing key limitations of hyperspectral data analysis, reviewing both foundational concepts and state-of-the-art techniques.