The rapid expansion of remote education has fundamentally transformed contemporary learning systems, generating extensive digital learning data and redefining instructional practices. This study investigates the impact of AI-enabled remote education on students’ academic performance using a machine learning–oriented analytical framework. Key factors examined include technological accessibility, AI-based learning support, student engagement, instructor–student interaction, self-regulated learning skills, satisfaction levels, and academic outcomes. Primary data were collected from 60 undergraduate students through a structured questionnaire. Descriptive and analytical techniques were applied, including percentage analysis, the Elbow Method to determine the optimal number of clusters, and K-Means clustering to classify students based on learning behavior, performance patterns, and satisfaction levels. The findings reveal that students with stable internet access and AI-supported learning platforms demonstrate improved academic performance and higher engagement levels. Conversely, technological barriers and limited interaction negatively affect learning outcomes. The study provides data-driven insights that can assist educators and institutions in optimizing AI-integrated remote education strategies to enhance academic achievement and learner satisfaction.