This study presents a systematic literature review of Life Cycle Assessment (LCA) methodologies applied to the principal constituents of Carbon Fibre Metal Laminates (CFMLs): aluminium, carbon fibres, and epoxy resin. CFMLs are increasingly utilised in aerospace and automotive sectors due to their favourable strength-to-weight ratio; however, their production is resource- and energy-intensive, and their composite structure poses significant challenges for end-of-life (EoL) management. This review maps the diversity of existing LCA approaches, revealing substantial heterogeneity in system boundaries, impact categories, and geographical representativeness. A strong regional focus on Asia, and China in particular, was identified in the case of aluminium, as almost half of the aluminium sources were in this geography. For carbon fibres and epoxy resins, the regional impact was even more pronounced, with 63% and 70% of publications originating from Europe, respectively, hence showing an underrepresentation of certain life cycle geography, such as bauxite mining regions. A key finding is the limited consideration of EoL scenarios, primarily due to difficulties in separating composite layers, which highlights the technical gap and need for a chemically or thermally separable intermediate layer for carbon fibre composites. Furthermore, the study compares traditional keyword-based literature searches with AI-driven tools (Undermind, You.com, Litmaps), demonstrating that AI-assisted methods substantially enhance the efficiency and comprehensiveness of literature retrieval. Notably, although Undermind contributed only 23% of the initial search results, it accounted for 39% of the publications ultimately selected for in-depth analysis. In contrast, the standard Web of Science (WoS) search exhibited the lowest precision, with merely 10% of its results deemed relevant for detailed review. Importantly, 70% of the total WoS search results were excluded following an initial human screening, which underlines the extensive filtering required to identify pertinent studies from broad database outputs. The findings highlight the higher efficiency of AI-supported search strategies in comparison to conventional approaches, underscoring their potential to optimise literature screening processes in LCA research while also revealing shortcomings in reproducibility, which must be addressed to ensure the maintenance of scientific standards.