A Systematic Analysis of Artificial Intelligence Coupling with Multi-objective Optimization in Managerial and Operational Decision Contexts

Organizations increasingly operate in complex systems that are dynamic, uncertain, data-rich, and tightly coupled, where performance must be balanced across multiple conflicting objectives and classical optimization alone often proves insufficient. Although research on the intersection of Artificial Intelligence (AI) and optimization has expanded rapidly, the literature remains fragmented across application domains and hybrid algorithm families. This paper presents a PRISMA-guided Systematic Literature Review (SLR) of 169 Scopus-indexed articles published between 2020 and January 2026. The search protocol adopts a precision-oriented Scopus strategy and supplementary eligibility screening to strengthen retrieval transparency. It examines how AI is coupled with multi-objective and goal-based optimization in managerial and operational decision contexts, the roles AI plays, the value it adds beyond classical optimization, and the decision problems for which different hybrid designs are most suitable. Combining descriptive mapping, thematic content analysis, evidence-quality interpretation, and cross-theme synthesis, the review organizes the evidence across six thematic domains, with the strongest concentration in manufacturing, transport and logistics, and energy and utilities. Four recurring AI roles emerge: predictive or representational enrichment, adaptive search, policy learning, and problem structuring. The findings show that AI creates its strongest value when classical optimization encounters four recurring bottlenecks: poor observability, combinatorial search complexity, dynamic sequential adaptation, and tightly coupled coordination. The evidence base is dominated by embedded, reinforcement-learning-intensive, multi-objective architectures, while goal-based models remain less frequent but still relevant in target-oriented and managerially interpretable settings. The review classifies major AI-optimization integration forms, links them to decision contexts, and assesses the maturity of major hybrid designs. The evidence is uneven across domains, with stronger cumulative support in manufacturing, transport, and energy than in healthcare and some service-oriented settings. While no single configuration is universally superior, future progress depends on more explainable, transferable, and implementation-ready AI-enhanced decision support systems.