Abstract

Garment fit is a key factor influencing consumer comfort, confidence, and purchasing decisions, particularly in the context of e-commerce, where the inability to try on products physically results in poor fit being a leading cause of costly product returns, material waste, and customer dissatisfaction. These challenges negatively impact not only consumer experience but also operational efficiency and environmental sustainability. However, traditional methods for evaluating garment fit are subjective, time-consuming, and inconsistent, highlighting the urgent need for more accurate, objective, data-driven, and scalable assessment solutions. This study presents a systematic literature review of research on garment fit assessments published between 2017 and 2025. The review emphasizes integrated approaches that combine fabric mechanical analysis, three-dimensional (3D) virtual simulation, and machine learning techniques to enhance the precision and efficiency of fit evaluation processes. A range of methodologies is examined and categorized into four major research areas: (1) 3D simulation and virtual try-on technologies; (2) data-driven machine learning and analytics; (3) material fabric–body interaction analysis; and (4) hybrid multi-technology integration frameworks. Results indicate that advanced tools such as 3D body scanning, simulation-based pattern adjustment, and predictive machine learning models have emerged as dominant technologies in recent studies. The review also identifies research gaps, such as a lack of standardized data formats, limited realism in fabric and body modeling, and poor interpretability of AI-driven systems. To address these limitations, the study proposes future research directions, including the development of open-access datasets, improvements in simulation fidelity, and enhancements in model explainability. Ultimately, these insights aim to inform the development of next-generation garment fit assessment frameworks that are accurate, transparent, personalized, and adaptable for practical use in the evolving digital fashion landscape.