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Human action recognition is a vital area of research in computer vision and machine learning, with applications spanning surveillance, healthcare, sports analysis, and human-computer interaction. This review presents a comprehensive overview of various human action recognition methods, highlighting their distinctive approaches and contributions to the field. We categorize these methods into segmentation-based, handcraft feature extraction, shape-based, motion-based, local binary pattern, and fuzzy logic approaches. Segmentation techniques focus on dividing the video into meaningful segments to isolate actions. Handcraft feature extraction involves manually designing features that capture relevant aspects of human motion. Shape-based methods analyze the silhouette or contour of the human body to identify actions, while motion-based methods focus on the dynamics of movement over time. Local binary pattern techniques leverage texture information for action recognition. Lastly, fuzzy logic approaches incorporate uncertainty handling and approximate reasoning to improve recognition accuracy. This review aims to provide insights into the strengths and limitations of each method, guiding future research towards more robust and efficient human action recognition systems.