<p>Hyperspectral target detection has become an essential research focus owing to its capability to identify and distinguish materials based on their unique spectral characteristics. Unlike conventional imaging systems that capture data in a few broad spectral bands, hyperspectral imaging acquires data across hundreds of contiguous narrow spectral bands, enabling a more detailed spectral representation of objects. This high spectral resolution facilitates precise target identification, even in scenarios with minimal visual contrast. The ability to detect small or obscured targets embedded within complex backgrounds makes hyperspectral imaging particularly valuable for applications such as remote sensing, military reconnaissance, environmental monitoring, mineral detection, and precision agriculture. However, the high dimensionality of hyperspectral data, the presence of mixed pixels, spectral variability, and background clutter pose significant challenges to accurate target detection. Consequently, researchers have increasingly focused on developing robust algorithms that integrate supervised learning, spectral-spatial feature extraction, and deep learning-based approaches. These methods aim to improve detection performance while addressing practical constraints such as limited labeled data and high computational demands. This review systematically investigates recent advancements in hyperspectral target detection, emphasizing the diverse methodologies, benchmark datasets, evaluation metrics, and open challenges discussed in the literature. By examining these studies, researchers can identify algorithmic strengths, select suitable models for specific datasets, and uncover gaps that warrant further exploration.</p>