Metaheuristics have lately gained popularity among researchers. Their underlying designs are inspired by biological entities and their behaviors, e.g. schools of fish, colonies of insects, and other land animals etc. They have been used successfully in optimization applications ranging from financial modeling, image processing, resource allocations, job scheduling to bioinformatics. In particular, metaheuristics have been proven in many combinatorial optimization problems. So that it is not necessary to attempt all possible candidate solutions to a problem via exhaustive enumeration and evaluation which is computationally intractable. The aim of this paper is to highlight some recent research related to metaheuristics and to discuss how they can enhance the efficacy of data mining algorithms. An upmost challenge in Data Mining is combinatorial optimization that, often lead to performance degradation and scalability issues. Two case studies are presented, where metaheuristics improve the accuracy of classification and clustering by avoiding local optima.