<![CDATA[The development of information technology in the Industrial Revolution 4.0 era has driven innovation in education, including the integration of Machine Learning (ML) into digital learning media. Elementary school students' low reading literacy is a major challenge, considering that this ability is the foundation for cognitive development and academic success. This study aims to analyze the effectiveness of ML-based digital learning media in improving elementary school students' reading literacy. The research used a pretest-posttest control-group design, with 15 students in the experimental class and 15 in the control class. The Decision Tree algorithm was used to analyze initial scores, classify literacy levels, and recommend reading materials based on students' abilities. The results showed a significant increase in the experimental class, with an average literacy score rising from 62.0 to 86.7 (+24.7 points), while the control class increased only from 59.2 to 64.5 (+5.3 points). The results of the paired sample t-test showed significant improvement in both classes (p < 0.05), with greater improvement in the experimental class. The independent sample t-test also showed a significant difference between the two groups (p < 0.05). These findings confirm that integrating ML and digital learning media can enable adaptive, personalized learning and real-time feedback. ML-based digital learning media has proven effective in significantly improving elementary school students' reading literacy.]]>
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