In the era of big data, the exponential growth in data volume, velocity, variety, and veracity has presented unprecedented challenges for traditional data processing and analytics techniques. In response to these challenges, metaheuristic algorithms have emerged as powerful tools for solving optimization problems in large-scale datasets. This paper provides a comprehensive review of the applications of metaheuristics in addressing various challenges posed by big data. We begin with an overview of big data challenges and the characteristics of metaheuristic algorithms. We then survey the literature on the application of metaheuristics in key areas such as data preprocessing, clustering, classification, association rule mining, and optimization. Furthermore, we discuss the scalability, efficiency, adaptability, and ethical considerations associated with the use of metaheuristic algorithms in big data analytics. Finally, we outline potential directions for future research in this rapidly evolving field. This review serves as a valuable resource for researchers, practitioners, and decision-makers interested in leveraging metaheuristic approaches to extract actionable insights from big data.