Integration Modes Between MCDM Methods and Machine Learning Algorithms: A Structured Approach for Framework Development

Decision-making is increasingly guided by the integration of Multi-Criteria Decision-Making (MCDM) and Machine Learning (ML) approaches. Despite their complementary strengths, the literature lacks clarity on which forms of integration exist, what contributions they offer, and how to determine the most effective form for a given decision problem. This study systematically investigates integration modes through a methodology that combines a literature review, expert judgment, and statistical analyses. It develops a novel categorization of integration modes based on methodological characteristics, resulting in five distinct modes: sequential approaches (ML → MCDM and MCDM → ML), hybrid integration (MCDM + ML), and performance comparison approaches, including ML vs. MCDM and ML vs. ML evaluated through MCDM. In addition, new evaluation criteria are introduced to ensure rigor, comparability, and reliability in assessing integration forms. By applying correspondence, cluster, and discriminant analyses, the study reveals distinctive patterns, relationships, and gaps across integration modes. The primary outcome is a novel evidence-based framework designed to guide researchers and practitioners in selecting the appropriate integration modes based on problem characteristics, methodological requirements, and application context. The findings reveal that sequential approaches (ML → MCDM and MCDM → ML) are most appropriate when efficiency, structured decision workflows, bias reduction, minimal human intervention, and the management of complex multi-variable decision problems are key objectives. Hybrid integration (MCDM + ML) is better suited to dynamic and data-rich environments that require flexibility, continuous adaptation, and a high level of automation. Performance comparison approaches are most appropriate for validation-oriented studies that evaluate outputs (MCDM[ML vs. ML]) and benchmark alternative methods (ML vs. MCDM), thereby supporting reliable method selection. Furthermore, the study underscores the predominance of integration modes that combine value-based MCDM methods with classification-based ML algorithms, particularly for enhancing interpretability. Environmental science and healthcare emerge as leading domains of adoption, primarily due to their high data complexity and the need to balance diverse, multi-criteria stakeholder requirements.