<p>Hyperspectral image (HSI) classification has achieved remarkable progress with the continuous advancement of deep learning techniques and sensor technology, enabling its application in a wide range of fields. Based on extensive research, our study is structured around four main aspects. Firstly, we provide a detailed and comprehensive review of machine learning and deep learning methods for HSI classification. Secondly, we explore several specific classification techniques in depth, including both traditional machine learning approaches and advanced deep learning models. Subsequently, we conduct an in-depth analysis of the experimental results, evaluating the performance of different methods both qualitatively and quantitatively to identify their respective strengths and limitations. Finally, we propose future research directions by summarizing the insights gained from the reviewed methods and using a taxonomy-based perspective to guide further advancements. This paper aims to show the latest developments, key characteristics, and persistent challenges in HSI classification techniques, providing valuable foundational knowledge and analytical perspectives for future studies in the field.</p>