Modern career planning requires relatively adaptive strategies that can keep up with rapid changes in technology, transformations in skill-related requirements, and more and more pronounced non-linear characterizations of career sequences. Fixed traditional recommendation systems cannot grapple with the variety and dynamics of contemporary career paths. This comprehensive review touches upon this very promising hybrid approach wherein multi-agent frameworks operate in conjunction with heterogeneous reasoning paradigms, knowledge graphs, and simulation-based modeling to achieve true personalization and adaptive career strategy development. The analysis synthesizes recent studies cutting across agent decision making, explainable AI (XAI), skill ontology mapping, temporal knowledge graph forecasting, and multi-agent persona-driven systems. Looking across fields, the study highlights noteworthy synergistic benefits, inherent hindrances, and practical opportunities towards linking these into one framework. The hybrid system proposed dynamically produces, evaluates, and perpetually updates career strategies through the interplay of simulation and multi-agent AI technologies. This area has long been the remotest of both fields, holding great promise toward developing more resilient career-planning systems that are truly aware of what the user wants at a certain point in time and that can adapt along with the evolution of the user’s desires and market demands.