In an increasingly competitive world, retail companies are implementing marketing strategies aimed at gaining customer preference by building lasting emotional relationships. This facilitates the conversion of so-called followers into loyal customers and brand ambassadors, thereby increasing return on investment (ROI), facilitating direct feedback to improve the product mix, and reducing customer acquisition costs over time, generating competitive advantages. The purpose of this systematic review is to analyze recent scientific production (2020-2025) on the application of Artificial Intelligence (AI) for the personalization of engagement in B2C business models, in order to analyze the predominant techniques, evaluate their impact on the consumer and determine the critical factors that condition long-term customer loyalty; the methodology used consisted of a systematic review in the main scientific databases in publications of high-impact journals. The main findings suggest that personalization tends to be dominated by machine learning models focused on algorithmic efficiency; however, a critical asymmetry was observed: the literature shows a hyper-focus on quantitative studies and functional efficiency, while studies on trust and ethics are the least relevant. Additionally, knowledge is biased by Asian dominance (53%) and the "novelty effect," limiting the applicability of strategies in markets with high demands for transparency, such as the American market. Furthermore, an emerging trend toward an Ethical-Relational Model has been observed in the literature. It is concluded that moving beyond the functional efficiency-centric approach and integrating Ethical AI as a strategic consideration may be key to fostering sustainable engagement, as evidenced by the reviewed literature.