<![CDATA[Fashion plays a crucial role in personal identity expression, particularly among Generation Z who are highly influenced by rapid trends and social media. This condition often leads to identity confusion and difficulty in finding an authentic personal style. This study aims to analyze and propose an intelligent personal style recommendation system based on artificial intelligence that integrates outfit selection, hairstyle recommendation, and makeup suggestions. A Systematic Literature Review (SLR) was conducted following the PRISMA 2020 guidelines by analyzing peer-reviewed studies published between 2020 and 2025 from Scopus, IEEE Xplore, and ScienceDirect. A total of 30 relevant studies were selected and thematically synthesized into several domains, including fashion recommendation systems, outfit compatibility modeling, virtual try-on technologies, skin tone analysis, makeup recommendation, and hairstyle classification. The results indicate that deep learning approaches such as Convolutional Neural Networks, Graph Neural Networks, hybrid filtering, and generative models significantly improve personalization accuracy and user satisfaction compared to conventional methods. The findings also reveal a research gap in the integration of multi-domain style recommendations within a single unified system. This study contributes by proposing a comprehensive AI-based personal stylist framework that supports holistic and inclusive personalization, helping users develop an authentic style aligned with their identity while navigating contemporary fashion trends.The abstract provides a general description of the contents of the paper and line spacing 1. The maximum length of the abstract is 250 words. If there are foreign terms that have not been standardized, they are written in italics. Citations are not permitted in the abstract section. Keywords : Artificial Intelligence, Fashion Recommendation System, Personal Style, Systematic Literature Review, Generation Z, Personalization.]]>
