Skill-based matching has become an important part of digital workforce management because organizations and employment platforms need to connect job requirements with workers who possess relevant competencies. Traditional approaches often depend on manual searching, job titles, resumes, or simple keyword filters, which may overlook differences in skill level, experience, availability, location, and other practical factors. This review examines existing research on skill-based matching, job recommendation, worker-job matching, and related workforce management systems. The reviewed approaches include keyword and content-based matching, similarity-based methods, knowledge-based and ontology-driven approaches, multi-criteria matching, context-aware recommendation, and hybrid systems. Recent systematic reviews identify continuing challenges involving skill representation, standardization, explainability, fairness, interoperability, and evaluation. The review also compares how different approaches represent workers and jobs and how they support ranking and recommendation. The findings indicate that skill matching becomes more useful when combined with contextual factors such as experience, availability, location, and job preferences. The paper concludes with research gaps and future directions for transparent and practical workforce-management systems.