This bibliometric analysis provides a comprehensive overview of pose estimation research, focusing on year-wise publication trends, average citations per year, top relevant sources, and country-wise distribution of publications. The study began with a comprehensive search in the SCOPUS database, utilizing keywords "Pose estimation" and "Pose-Estimation," resulting in 20,594 records. The dataset was refined to the Social Science category, yielding 459 relevant records. Documents were categorized by type, with a focus on articles, resulting in 103 articles for analysis. Language filtering led to 98 English, 4 Chinese, and 1 German articles. English articles were further classified by source type and access, predominantly from journals with Open Access being dominant. This analysis offers insights into the current state of pose estimation research, emphasizing its interdisciplinary nature and global collaboration dynamics. Findings provide a foundation for future research directions and underscore the profound impact of pose estimation technologies across various domains, including sports science, robotics, and human-computer interaction.