<p>This article presents a systematic literature review (SLR) on the use of meta-learning for the management of metaheuristics to solve dynamic combinatorial optimization problems (DCOP), as well as the role of emerging technologies such as quantum computing, multicore computing, and max-plus algebra in this context. The review was conducted following a modified version of Kitchenham’s methodology, formulating research questions, and applying inclusion and exclusion criteria to more than 500 publications between 2018 and 2025, allowing the selection of 22 relevant articles. The results show that meta-learning has been used mainly to improve the performance of optimization algorithms in dynamic environments through knowledge transfer between historical scenarios, although approaches that automate the selection or configuration of metaheuristics for DCOP have not yet been sufficiently explored. In the case of quantum computing, algorithms inspired by techniques such as annealing and particle swarm optimization (PSO) were identified and successfully applied to dynamic problems, although with little implementation in actual quantum hardware. Regarding max-plus algebra, relevant proposals for planning problems in discrete systems are recognized, but without considering the distributed nature or uncertainty inherent in many DCOPs. Finally, no studies were found that apply multicore computing specifically to DCOP, despite its potential. Future lines of research include the development of hybrid meta-learning and hyper-heuristic frameworks, the incorporation of quantum algorithms for metaheuristic management, and the exploration of parallelization and robust modeling techniques in dynamic and uncertain environments.</p>