Master Data Management (MDM) has become a strategic capability for organizations seeking to establish consistent, accurate, and trusted enterprise data that supports operational efficiency, informed decision-making, and regulatory compliance. As organizations continue to expand across digital ecosystems involving cloud computing, artificial intelligence, big data analytics, and distributed enterprise applications, the complexity of managing critical business entities such as customers, products, suppliers, employees, and financial records has increased significantly. While technological advancements have enhanced MDM capabilities, successful implementation depends heavily on effective leadership strategies that align governance structures, organizational culture, business objectives, and technology investments. This study presents a systematic evidence mapping of leadership strategies for Enterprise Master Data Management by synthesizing existing research on governance models, executive sponsorship, organizational leadership, data stewardship, change management, cross-functional collaboration, and MDM implementation practices. The evidence mapping systematically categorizes published literature according to leadership approaches, governance frameworks, organizational roles, implementation methodologies, enabling technologies, industry applications, and evaluation metrics. The analysis identifies executive commitment, clearly defined governance structures, strategic data stewardship, stakeholder engagement, standardized data policies, metadata management, and continuous data quality improvement as the primary factors contributing to successful MDM initiatives. The findings further demonstrate that effective leadership fosters organizational accountability, promotes enterprise-wide collaboration, accelerates digital transformation, strengthens regulatory compliance, and enhances the consistency of master data across heterogeneous information systems. Additionally, the study highlights the increasing integration of artificial intelligence, machine learning, cloud-native MDM platforms, metadata automation, intelligent data catalogs, and analytics-driven governance into modern enterprise MDM programs. Despite these advancements, several challenges persist, including organizational resistance to change, fragmented data ownership, legacy system integration, inconsistent governance practices, scalability constraints, and evolving regulatory requirements. The evidence mapping also identifies promising future research directions involving autonomous Master Data Management, AI-assisted data stewardship, knowledge graph–based master data models, explainable artificial intelligence for governance, real-time master data synchronization, and intelligent compliance automation. Overall, this study provides researchers, practitioners, enterprise architects, and organizational leaders with a comprehensive overview of leadership strategies that influence successful Master Data Management programs and offers practical insights for designing resilient, scalable, governance-driven, and business-aligned MDM initiatives that improve enterprise data consistency, operational excellence, and long-term organizational value.