The rapid expansion of information production presents a growing challenge in identifying high-quality, relevant studies necessary for a solid research foundation in Systematic Literature Reviews (SLR). Traditional methods often struggle with academic publications’ increasing volume and dynamic nature, necessitating more efficient analytical tools. Artificial Intelligence (AI) and Natural Language Processing (NLP) offer significant potential in streamlining literature reviews through advanced analytical techniques. This work explores how AI and NLP tools enhance article screening and information synthesis, particularly through Retrieval-Augmented Generation (RAG). While large language models demonstrate strong text generation capabilities, they frequently lack comprehensive contextual understanding. RAG addresses this limitation by retrieving precise information, enabling more accurate and context-aware literature reviews. A novel approach is proposed that transforms the traditionally linear SLR workflow into a dynamic, continuously updatable “bundle”— a unified framework that integrates search, screening, data extraction, and synthesis. This approach is inspired by the RAPTOR Python package, which recursively embeds, clusters, and summarizes text to construct a hierarchical knowledge structure. The adapted model, DSAɪVE, extends RAPTOR’s capabilities to improve contextual summarization and structured synthesis, enhancing its applicability in multidisciplinary research fields. To demonstrate the effectiveness of this approach, a case study examines the potential of methanol as an alternative fuel in transport systems. The results highlight how AI-driven methodologies facilitate large-scale literature synthesis and knowledge integration. By leveraging AI, this work contributes to developing more efficient, systematic, and scalable literature review processes, addressing a critical challenge in modern research.