Hyperspectral imaging (HSI) captures detailed spectral and spatial information across hundreds of contiguous wavelengths, providing unprecedented data for analysis. However, HSI data is high-dimensional, imposing challenges for storage, processing and analysis. Autoencoders (AE) are unsupervised deep learning models that learn efficient data encoding in lower dimensional spaces while capturing salient features. Integrating HSI and AE provides a powerful solution by leveraging AE’s dimensionality reduction and feature learning capabilities on HSI’s rich data. This paper reviews the integration of these two technologies, covering background, motivation, applications in classification, unmixing and anomaly detection, hyperparameter tuning, and future research directions. The combined HSI-AE approach unlocks new possibilities across domains like agriculture, medical imaging, remote sensing and environmental monitoring.