<p>This paper presents a comparative review of the meta-heuristic-based workflow scheduling algorithms in a cloud computing environment. There are several workflow applications in both industry and academia that require distributed computations such as cloud computing resources. The task scheduling module in such systems is a prominent component that affects underlying resource utilization and also the delivered Quality of Service (QoS) to subscribers. Although there are miscellaneous meta-heuristic optimization algorithms, the most efficient and applicable in task scheduling ambit are selected. To this end, a subjective classification is presented; then, the state-of-the-art published in authentic publications dating to the last five years are excerpted based on the presented taxonomy. This taxonomy classifies meta-heuristic algorithms into three local-based, global-based, and hybrid approaches. These algorithms are extended in favor of users, cloud providers, or both as prominent stakeholders in cloud systems; the reason why the taxonomy goes through single-objective and multi-objective viewpoints. The literatures are compared according to the related evaluation scheduling metrics and environments. This paper highlights for interested researchers the potential and existing gaps for improvement and further development of future task scheduling techniques in cloud computing platforms.</p>