Metaheuristic algorithms have gained popularity in addressing various optimization challenges in diverse fields such as digital image processing, energy, machine learning, robotics, and data analytics. Most metaheuristic methods are population-based, where a population of search agents (or individuals) explores different candidate solutions within a solution space. These optimization frameworks offer several advantages, including the interaction between individuals, which is crucial for effectively exploring the search space and overcoming local optima. Metaheuristic algorithms must balance exploring the search space for promising new solutions and exploiting regions containing high-quality solutions. Clustering and partitioning help divide the search space into smaller, more manageable regions, allowing effective exploration and intensive exploitation of promising regions. These techniques can be employed in a variety of metaheuristic algorithms. This chapter provides a comprehensive review of clustering and partitioning techniques within metaheuristic algorithms. Its purpose is to provide a detailed analysis of these techniques, identify their applications, advantages, and limitations, and facilitate their understanding and use in optimization problems.