<![CDATA[This study aims to analyze the trends, strategies, and implications of digital and data-driven educational budgeting on learning quality improvement through a Systematic Literature Review (SLR) approach. The review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol. Literature was regularly retrieved from Google Scholar, Scopus, ERIC, Semantic Scholar, and SINTA databases covering publications from 2019 to 2025. Following the screening and eligibility process, 18 articles met the inclusion criteria and were analyzed using thematic synthesis. The findings reveal three major themes. First, the digital transformation of budgeting documents through data-based RKAS and ARKAS enhances transparency, accountability, and the effectiveness of educational resource allocation. Second, the utilization of big data analytics and artificial intelligence in educational financial planning enables more predictive, accurate, and responsive decision-making processes aligned with learning needs. Third, data-driven budgeting demonstrates a positive relationship with learning quality improvement by optimizing resource allocation toward priority educational programs. The review further indicates that the success of data-driven budgeting depends not only on technological adoption but also on human resource capacity, organizational culture, and supportive policy environments. This study reinforces the theory of data-based decision making and proposes the concept of budgeting as a pedagogical policy instrument in contemporary educational management. .]]>
