Personalized Federated Learning (PFL) is an advanced machine learning approach that customizes global models to individual user data while maintaining privacy through decentralized training. This study presents a bibliometric analysis of PFL research using data sourced from the Scopus database. Three bibliometric tools—Biblioshiny, VOSviewer, and CiteSpace—were employed to examine publication trends, author impact, thematic evolution, and scholarly networks. The analysis highlights a sharp rise in annual scientific production, indicating rapid growth in the field since 2020. Most relevant authors and sources, including Chen Zihan and IEEE Transactions on Mobile Computing, demonstrate concentrated influence in shaping the domain. Thematic mapping reveals well-developed core themes such as personalization and data privacy, alongside emerging topics like federated reinforcement learning and hypernetworks. Network visualizations of co-cited authors and keyword co-occurrences provide insights into intellectual structure and evolving research priorities. Identified research gaps and underexplored themes suggest practical implications for expanding PFL into diverse domains such as IoT, healthcare, and privacy-preserving edge intelligence.