LinkedIn has emerged as a dominant platform for professional networking and career development, yet bibliometric analyses on its scholarly landscape remain scarce. This study systematically maps LinkedIn research using 1,273 peer-reviewed publications from Web of Science (WoS), following the SPAR-4-SLR protocol. To address four core research questions, we analyze (1) thematic structures and evolution, (2) collaboration and citation networks, (3) publication venues and citation metrics, and (4) emerging trends. Key bibliometric indicators—total citations (25,461), h- index (38), and publication trends—were analyzed, while co-citation and bibliographic coupling (WoS) and keyword co-occurrence (Scopus) network analyses were conducted using VOSviewer. Results reveal a sharp publication increase, peaking at 204 in 2023, with Computers in Human Behavior (19 papers, 898 citations) and PLOS One (10 papers, 897 citations) as leading outlets. Research clusters focus on recruitment, professional branding, and LinkedIn’s role in organizations, though empirical validation remains limited, particularly regarding career outcome predictions. Findings offer a structured knowledge base for academia and industry. Limitations include reliance on WoS for citations and Scopus for keywords, potentially introducing data set inconsistencies. Future research should integrate cross-database approaches and explore LinkedIn’s role in AI-driven recruitment.