Manufacturing Execution Systems (MES) have become a crucial intermediary between Enterprise Resource Planning (ERP) systems and shop-floor automation. Playing a vital role in Industry 4.0. As industrial processes become increasingly interconnected, MES are evolving beyond traditional monitoring and scheduling to incorporate AI-driven decision-making, real-time analytics, and adaptive manufacturing capabilities. Despite its growing importance, MES research remains fragmented, requiring a systematic review to identify key trends, research gaps, and future research directions. This study conducts a Systematic Literature Review (SLR) using bibliometric analysis with VOSviewer to map MES research. It identifies three major research clusters: Core MES Technologies, MES in Industry 4.0, and MES vs. Alternative Systems. The findings highlight key challenges, including high implementation costs, integration complexities, cybersecurity risks, and interoperability barriers, particularly in MES-ERP-SCADA integration. The study underscores the necessity of AI-driven MES solutions, modular architectures, and blockchain-based security frameworks to optimize digital transformation in manufacturing. By providing a structured overview of MES advancements and challenges, this research contributes both theoretically and practically. It offers insights into MES standardization, sustainability-driven MES adoption, and regulatory compliance, alongside recommendations for future research in predictive AI-based MES, cloud-security frameworks, and scalable MES solutions for small and medium-sized enterprises (SMEs). This study serves as a foundation for academics, industry professionals, and policymakers to drive the sustainable evolution of MES within smart manufacturing ecosystems.