Social protection, which involves various public and private efforts to support income or consumption for people experiencing poverty and to protect vulnerable groups from livelihood risks, is essential for reducing poverty and social exclusion. The COVID-19 pandemic highlights the importance of resilient social protection systems and the critical role of data in their practical implementation. Many countries face challenges due to fragmented data landscapes, which lead to inefficiencies and social issues. This systematic literature review aims to synthesise how Master Data Management (MDM) is applied within social protection data ecosystems and to develop design guidance for Malaysia, identifying models, challenges, and enablers for effective implementation. The study, guided by the PRISMA 2020 guidelines, conducts a comprehensive search of academic databases and web sources, including structured keyword searches, article screening, application of inclusion/exclusion criteria, and thematic synthesis. A total of 32 academic and technical publications out of 226 were analysed from 2021 to 2025. Findings show progress in MDM for social protection, with trends towards technology-enabled models, integrated registries, and maturity models. Challenges include technical issues like system integration and data quality, organisational barriers like silos and resistance, and socio-political problems such as digital exclusion and legal uncertainties. Enablers include strong data governance, modern technologies, and change management. The study highlights the need for an MDM framework in the social protection data ecosystem, linking models to outcomes and proposing a federated blueprint aligned with Malaysia's semi-centralised system, such as PADU and PDPS. This unified view tackles technical, organisational, and socio-political challenges, supporting better data management, integration, and use across agencies and programmes.