The Retrieval-Augmented Generation (RAG) marks a turning point in natural language processing (NLP), leveraging the combined power of large-scale generative models and document retrieval architectures. In this review, we comprehensively analyze the development of RAG frameworks, explicitly focusing on their utility in solving societal problems, namely in improving women’s empowerment and social justice. In this paper, we take a data-driven approach to understanding RAG from the perspective of recent studies and point out the methodological advancements made in tiered retrieval models, the dynamic relevance of documents and more modalities. Additionally, the paper explores the adaptation of RAG systems across various fields, from education to health care and governance, emphasizing their role in empowering marginalized communities. Particular emphasis is placed on ontological knowledge models and data-driven systems utilizing RAG for comprehensive evaluation and decision-making processes. This review also investigates the challenges and ethical considerations about implementing RAG systems for sensitive applications, such as privacy, data bias, and alignment with social objectives. By exploring state-of-the-art frameworks and case studies, this paper aims to build awareness about the potential opportunity of enhancing RAG-based NLP systems, leading toward inclusive growth and better resource accessibility by guiding the reader toward future works. These results highlight the RAG framework as a powerful tool for bridging societal gaps via sophisticated AI technologies.