Abstract: As large-scale astronomical surveys, such as the Sloan Digital Sky Survey (SDSS) and the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST), generate increasingly complex datasets, clustering algorithms have become vital for identifying patterns and classifying celestial objects. This paper systematically investigates the application of five main categories of clustering techniques—partition-based, density-based, model-based, hierarchical, and “others”—across a range of astronomical research over the past decade. This review focuses on the six key application areas of stellar classification, galaxy structure analysis, detection of galactic and interstellar features, high-energy astrophysics, exoplanet studies, and anomaly detection. This paper provides in-depth analysis of the performance and results of each method, considering their respective suitabilities for different data types. Additionally, it presents clustering algorithm selection strategies based on the characteristics of the spectroscopic data being analyzed. We highlight challenges such as handling large datasets, the need for more efficient computational tools, and the lack of labeled data. We also underscore the potential of unsupervised and semi-supervised clustering approaches to overcome these challenges, offering insight into their practical applications, performance, and results in astronomical research.