This review provides an extensive overview of hyperspectral image classification and prediction, focusing on techniques and challenges. Hyperspectral image processing (HSIP) is crucial in remote sensing, enabling accurate and precise analysis of an object's spectral information. Hyperspectral images consist of hundreds of spectral bands, capturing vast amounts of information about the Earth's surface. Accurate classification and prediction of land cover in these images are vital for understanding ecosystems and human impacts. Deep learning techniques have significantly improved the efficiency and accuracy of hyperspectral image analysis, allowing precise land cover and land use/land cover (LULC) prediction. Image classification is challenging due to the large number of data samples and limited labels. Selecting appropriate bands can yield better classification results. This review covers various aspects of hyperspectral image processing, including preprocessing, feature extraction and selection, classification methods, and prediction methods. It also discusses datasets, evaluation metrics, and challenges in hyperspectral image classification. The review aims to benefit new researchers in the field by providing a comprehensive understanding of the techniques and challenges involved in hyperspectral image classification. Hyperspectral imaging (HSI) has evolved since the 1970s, with early systems becoming commercially available in the 1990s. HSI captures detailed electromagnetic spectra, providing information on physical and chemical properties of objects. It is non-invasive and useful in agriculture, mineralogy, and environmental monitoring. HSI sensors capture numerous spectral bands, allowing for more detailed analysis than traditional RGB imaging. HSI is used in remote sensing, environmental monitoring, geology, and urban planning. Multispectral images (MSI) capture more spectral bands than RGB images, typically ranging from 3 to 10 bands. Examples include visible green, red, blue, and infrared bands. HSI images are captured using multiple narrow spectral bands, forming a three-dimensional data cube. HSI is valuable for various applications, including remote sensing and medical imaging. Remote sensing data for HSI is collected using platforms such as aircraft, satellites, balloons, rockets, and space shuttles. Spaceborne and airborne sensors are commonly used for capturing HSI. Examples of airborne sensors include AVIRIS, CASI, HYDIC, DAIS, PHI, HyMap, APEX, MAIS, and UAS. Spaceborne sensors include MERIS.This review provides an extensive overview of hyperspectral image classification and prediction, focusing on techniques and challenges. Hyperspectral image processing (HSIP) is crucial in remote sensing, enabling accurate and precise analysis of an object's spectral information. Hyperspectral images consist of hundreds of spectral bands, capturing vast amounts of information about the Earth's surface. Accurate classification and prediction of land cover in these images are vital for understanding ecosystems and human impacts. Deep learning techniques have significantly improved the efficiency and accuracy of hyperspectral image analysis, allowing precise land cover and land use/land cover (LULC) prediction. Image classification is challenging due to the large number of data samples and limited labels. Selecting appropriate bands can yield better classification results. This review covers various aspects of hyperspectral image processing, including preprocessing, feature extraction and selection, classification methods, and prediction methods. It also discusses datasets, evaluation metrics, and challenges in hyperspectral image classification. The review aims to benefit new researchers in the field by providing a comprehensive understanding of the techniques and challenges involved in hyperspectral image classification. Hyperspectral imaging (HSI) has evolved since the 1970s, with early systems becoming commercially available in the 1990s. HSI captures detailed electromagnetic spectra, providing information on physical and chemical properties of objects. It is non-invasive and useful in agriculture, mineralogy, and environmental monitoring. HSI sensors capture numerous spectral bands, allowing for more detailed analysis than traditional RGB imaging. HSI is used in remote sensing, environmental monitoring, geology, and urban planning. Multispectral images (MSI) capture more spectral bands than RGB images, typically ranging from 3 to 10 bands. Examples include visible green, red, blue, and infrared bands. HSI images are captured using multiple narrow spectral bands, forming a three-dimensional data cube. HSI is valuable for various applications, including remote sensing and medical imaging. Remote sensing data for HSI is collected using platforms such as aircraft, satellites, balloons, rockets, and space shuttles. Spaceborne and airborne sensors are commonly used for capturing HSI. Examples of airborne sensors include AVIRIS, CASI, HYDIC, DAIS, PHI, HyMap, APEX, MAIS, and UAS. Spaceborne sensors include MERIS.