Nain, GarimaGarimaNainSamal, UmashankarUmashankarSamal2025-12-312025-12-312026-0610.1016/j.techsoc.2025.1031952-s2.0-105025010347http://repository.iitgn.ac.in/handle/IITG2025/33746Artificial intelligence (AI) is among the key technologies reshaping modern industries by establishing mass customization and personalization. It has improved the consumer experience by allowing the individualization of products and services on a large scale. Over the past decade, there has been a tremendous focus on research in this domain in both academia and industry. Therefore, this work provides a comprehensive literature analysis of 164 peer-reviewed articles, determined through the SPAR-4-SLR methodology, ensuring transparency and reproducibility. The publication trends, influential contributors, and major research themes are identified through R and VOSviewer tools. The keyword co-occurrence (cluster) analysis depicts the key themes such as intelligent product design and customization, smart manufacturing and cyber–physical integration, AI-enhanced learning and commercial personalization, and additive manufacturing and AI-driven fabrication. The overlay visualization of and systematic content-based taxonomy provides an in-depth examination of literature from various perspectives. The theoretical and managerial implications suggested are data-driven and intelligence-embedded product configuration, Adaptive manufacturing, Cognitive personalization, Human-AI collaboration, Individualized value creation, Agile supply chains, And long-term customer loyalty. Future research in AI-enabled mass customization and personalization emphasizes achieving the industry 5.0 era of individualization. Thus, Edge-AI, Human-centric design, Adaptability, Explainability, Sustainability, Societal, And ethical attributes must be prioritized.en-USArtificial intelligenceDeep learningMass customizationMass personalizationIndustry 5.0Article0WOS:001644143600001