<![CDATA[Digital transformation is reshaping management accounting, yet existing reviews commonly examine digitalization, business intelligence, accounting technologies, or professional change as separate streams. They therefore provide limited explanation of how technological infrastructure, management control, information trust, professional roles, and analytical competence jointly enable the transition from retrospective reporting to strategic analytics. A systematic literature review (SLR) was selected because the evidence is interdisciplinary, conceptually heterogeneous, and methodologically dispersed, requiring a transparent synthesis rather than an additional single-context empirical study. Following PRISMA 2020, this study identified, screened, quality-assessed, coded, and synthesised 45 peer-reviewed Scopus-indexed articles published between 2018 and 2026. The analysis combines descriptive mapping with deductive-inductive thematic synthesis. Five interconnected themes emerged: the transition from reporting to forward-looking analytics; the transformation of management accountants into strategic interpreters and business partners; the layered adoption of ERP, cloud systems, business intelligence, artificial intelligence, and automation; the importance of decision quality, information trust, and explainability; and the development of digital, analytical, and communication competencies. The study’s novelty lies in an integrated Digital Management Accounting Transformation Framework that links three mutually dependent dimensions: digital information infrastructure, analytics-enabled control and decision processes, and professional interpretive capability. Theoretically, the framework extends digital management accounting literature beyond technology-adoption explanations by conceptualising transformation as a socio-technical reconfiguration of information, control, and professional judgement. Practically, it indicates that organisations, professional bodies, and policymakers should combine technology investment with data governance, competency development, cross-functional collaboration, and mechanisms for validating algorithmic outputs. ]]>
