This chapter provides an extensive review of hyperspectral image classification and prediction techniques, highlighting the importance of hyperspectral imaging (HSI) in remote sensing. HSI, which captures detailed spectral information across hundreds of bands, offers significant advantages over traditional RGB and multispectral imaging. The technology has evolved from early NASA developments in the 1970s to become more affordable and accessible, with applications in agriculture, mineralogy, and environmental monitoring. The chapter discusses the challenges and benefits of HSI, emphasizing its ability to provide nuanced analysis and detailed spectral profiles. It also covers the various platforms used for collecting HSI data, such as aircraft, satellites, and balloons, and outlines the key components of HSI processing, including image pre-processing, feature extraction, and classification methods. Additionally, the chapter addresses the datasets, evaluation metrics, and challenges in HSI, making it a valuable resource for new researchers in the field.This chapter provides an extensive review of hyperspectral image classification and prediction techniques, highlighting the importance of hyperspectral imaging (HSI) in remote sensing. HSI, which captures detailed spectral information across hundreds of bands, offers significant advantages over traditional RGB and multispectral imaging. The technology has evolved from early NASA developments in the 1970s to become more affordable and accessible, with applications in agriculture, mineralogy, and environmental monitoring. The chapter discusses the challenges and benefits of HSI, emphasizing its ability to provide nuanced analysis and detailed spectral profiles. It also covers the various platforms used for collecting HSI data, such as aircraft, satellites, and balloons, and outlines the key components of HSI processing, including image pre-processing, feature extraction, and classification methods. Additionally, the chapter addresses the datasets, evaluation metrics, and challenges in HSI, making it a valuable resource for new researchers in the field.