<![CDATA[This study aims to analyze the implementation of the deep learning approach in mathematics subjects in elementary schools based on the study of relevant scientific articles. This study uses a literature study method. The data source was obtained from the Google Scholar, ERIC, and ScienceDirect electronic databases with a publication range of 2018 to 2025. The keywords used include Deep Learning, primary education, mathematics, joyful learning, mindful learning, and meaningful learning. The data analysis technique used is content analysis. The results of the study show that the implementation of deep learning in elementary school mathematics still faces conceptual ambiguity between the meaning of technology and pedagogy. The trend of adoption is dominated by developed countries such as China and South Korea with CNN, RNN, LSTM, and transformer architectures. Implementation challenges include three main dimensions: technical (dataset complexity, overfitting, infrastructure limitations), pedagogical (lack of teacher training, rigid curriculum, unbalanced teacher-student ratio), and ethical (data privacy, algorithmic bias, access gap). Opportunities identified include personalization of learning, early detection of learning difficulties, automation of assessments, and the development of holistic competencies of students through the integration of joyful, mindful, and meaningful learning. This study concludes that the successful implementation of deep learning in elementary school mathematics requires conceptual clarification, teacher capacity strengthening, contextual adjustment, and the development of pedagogical models rooted in local wisdom.]]>
