During the last years, research in applying machine learning (ML) to design efficient, effective and robust metaheuristics became increasingly popular. Many of those data driven metaheuristics have generated high quality results and represent state-of-the-art optimization algorithms. Although various appproaches have been proposed, there is a lack of a comprehensive survey and taxonomy on this research topic. In this paper we will investigate different opportunities for using ML into metaheuristics. We define uniformly the various ways synergies which might be achieved. A detailed taxonomy is proposed according to the concerned search component: target optimization problem, low-level and high-level components of metaheuristics. Our goal is also to motivate researchers in optimization to include ideas from ML into metaheuristics. We identify some open research issues in this topic which needs further in-depth investigations.