<p>Hyperspectral imaging (HSI) is an emerging technology with applications across medicine, defence, mineralogy, forensics, food processing, and remote sensing. Each pixel in an HSI contains rich spectral information, often representing a mixture of multiple material classes due to low spatial resolution and atmospheric effects. This makes accurate classification challenging. Hyperspectral Image Unmixing (HIU) techniques aim to resolve this issue by identifying constituent materials and estimating their fractional abundances within each pixel. Effective unmixing significantly enhances classification and prediction accuracy in real-world applications. This review presents a comprehensive overview of hyperspectral image analysis, covering key components such as preprocessing, dimensionality reduction, unmixing algorithms—including multimodal approaches—and classification models. We also discuss current challenges and highlight future research directions to guide researchers entering this evolving field.</p>