Purpose The purpose of this article is to present a thorough analysis of the latest developments, uses and prospects of additive manufacturing (AM), highlighting its ability to facilitate complicated geometries, customisation and environmentally friendly production across a range of sectors. In addition, it explores new trends like multi-material printing, artificial intelligence–driven process optimisation and Industry 4.0 integration while identifying current issues like scalability, standardisation and material constraints. Design/methodology/approach A thorough literature assessment of works published between 2020 and 2025 was carried out, with an emphasis on industrial applications, technical advancements and sustainability viewpoints. Comparisons between AM and traditional production were made, emphasising the advantages and disadvantages unique to each industry. To improve analytical depth, interpretive commentary was added to figures and tables. Findings This study shows that AM provides notable gains in energy efficiency, weight reduction and design flexibility, especially in the automotive and aerospace industries. It makes sustainable building techniques, personalised electronics and patient-specific healthcare solutions possible. Notwithstanding its promise, problems with certification, scalability and material variety prevent broad implementation. Practical implications Researchers and industry practitioners looking to optimise AM techniques and incorporate them into mainstream production might benefit from the results’ practical implications. The assessment outlines strategies for creating hybrid AM systems, strengthening the robustness of supply chains and quickening attempts to standardise across international sectors. Originality/value This work sets itself apart by providing a critical examination of new difficulties and an application-oriented synthesis of current advancements in AM with quantifiable industry insights. By linking current research trends with future directions, it fills in existing knowledge gaps and provides a roadmap for moving AM towards next-generation commercial applications.
M. et al. (2026) studied this question.