This paper conducted a systematic literature review of 128 highly cited research papers on Retrieval-Augmented Generation (RAG) published from 2020 to May 2025. Papers were collected from databases such as the ACM Digital Library, IEEE Xplore, Scopus, ScienceDirect, and DBLP and analyzed based on the PRISMA 2020 framework. This paper categorizes RAG datasets, architectures, and evaluation methods, and comprehensively analyzes the empirical evidence on the effectiveness and limitations of RAG to clarify current research trends, highlight methodological gaps, and suggest directions for future research priorities. To mitigate citation delay bias, a low citation threshold was applied to papers published in 2025.