<![CDATA[The rapid growth of social media in the digital era has fundamentally transformed fashion marketing and consumer engagement, enabling businesses to adopt more personalized and data-driven strategies. Despite the expanding availability of big data, many organizations continue to face challenges in translating social media information into meaningful strategic insights. This article presents a narrative review of recent empirical and conceptual studies to examine how data analytics, Artificial Intelligence (AI), and predictive modeling are being applied to optimize fashion marketing performance within digital environments. The review synthesizes findings from peer-reviewed literature on Natural Language Processing (NLP), Latent Dirichlet Allocation (LDA), sentiment analysis, Game Theory, and machine learning algorithms, including XGBoost and Random Forest. Evidence drawn from reviewed studies indicates a significant positive relationship between digital engagement indicators and sales performance, with predictive models achieving accuracy rates as high as 94.73% in identifying high-engagement content (Ju, 2024). Game Theory modeling further suggests that sustained aggressive social media engagement strategies can yield a competitive market share advantage of approximately 30% over passive competitors under certain conditions (Ju, 2024). However, the reviewed literature consistently identifies a “personalization–privacy paradox,” in which highly personalized marketing strategies may simultaneously increase consumer discomfort regarding privacy and data usage. This review concludes that data analytics and AI have become critical instruments in the transition from mass marketing toward micro-targeting approaches in fashion. Sustainable success in digital fashion marketing depends on balancing technological innovation, ethical transparency, and human creativity to maintain long-term consumer trust and competitive advantage.]]>
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