Abstract
Decision-making in complex, multifaceted scenarios has become increasingly critical across diverse sectors, necessitating robust frameworks like Multi-Criteria Decision-Making (MCDM). Over the past two decades (2004–2024), MCDM has transformed from foundational methods like AHP and TOPSIS into dynamic hybrid models integrating artificial intelligence, fuzzy logic, and machine learning. Despite significant strides, the field faces challenges in addressing geographic disparities, underexplored domains, and adapting to emerging global needs. This study provides a comprehensive review of MCDM's evolution, consolidating insights from 3,655 peer-reviewed articles sourced through Dimensions.ai and analyzed using bibliometric tools like VOSviewer. The research identifies publication trends, leading contributors, thematic clusters, and collaborative networks while pinpointing gaps and opportunities for future exploration. These key findings highlight exponential growth in MCDM applications, particularly in sustainable energy, urban planning, and healthcare optimization. These advancements align with global priorities, including the United Nations Sustainable Development Goals (SDGs) such as clean energy, climate action, and sustainable cities. However, critical gaps remain in addressing issues like poverty alleviation, gender equity, and biodiversity conservation, emphasizing the need for broader interdisciplinary applications. This review concludes that MCDM's potential lies in embracing inclusivity, advancing into emerging technologies like blockchain and the metaverse, and fostering collaboration across underrepresented regions and domains. By harnessing real-time data, immersive simulations, and secure decision-making platforms, MCDM can redefine how global challenges are addressed.