A Review of Leveraging Artificial Intelligence for Real-Time Fashion Trend Forecasting: Analyzing Social Media and Sales Data to Enhance Design Innovation and Retail Strategy

Abstract

Artificial intelligence has become an essential component of modern industries, signif icantly influencing how businesses operate in the 21st century. The fashion industry, known for its dynamic and fast-changing nature, is no exception. Fashion trends evolve rapidly, with new styles spreading almost instantly through digital platforms. Social media platforms such as Instagram, TikTok, Pinterest play a major role in shaping consumer preferences, purchasing decisions, and trend adoption. Despite this shift, many fashion brands still rely on traditional forecasting methods based on historical sales data, seasonality, and expert judgment. These conventional approaches often result in delayed responses to emerging trends, inaccurate demand forecasts, excess inventory and limited design innovation. With the growing availability of real-time data from social media interactions, online sales, there is a critical need for timely and effective analytical solutions to process such data. Addressing this need, the present study conducts a comprehensive review of AI applications used to enhance real-time fashion trend forecasting by integrating social media and sales data. The review examines various AI techniques employed in previous studies, explores data collection and processing methods, evaluates the strengths and limitations of existing approaches. It also identifies key challenges, including data inconsistency, lack of standardized evaluation metrics, limited model transparency, and ethical concerns related to data usage. Following internationally recognized review practices, a systematic literature search was carried out using IEEE Xplore, ResearchGate, and Google Scholar, covering studies published between 2013 and 2025. Overall, this review provides valuable insights for fashion researchers, designers, retail strategists, highlighting research gaps and supporting the development of more accurate, efficient, sustainable AI-driven forecasting systems for the fashion industry.