This study explores recent advances in unsupervised learning algorithms for classifying animal behavior, addressing limitations of traditional behavioral tests and pose-estimation tools. By comparing four algorithms—B-SOiD, BFA, VAME, and Keypoint-MoSeq—the authors provide insights into their clustering efficiency, methodological differences, and biological relevance. These findings offer critical guidance for researchers seeking to apply machine learning in behavioral neuroscience, paving the way for unbiased, scalable analysis of complex behaviors and advancing understanding of the neural mechanisms underlying naturalistic behavior.