Deep Neural Networks (DNNs) have achieved state-of-the-art performance across numerous domains, yet their efficacy is intrinsically tied to the selection of optimal hyperparameters, a process known as Hyperparameter Optimization (HPO). Traditional exhaustive search methods, such as Grid Search (GS) and Random Search (RS), suffer from prohibitive computational costs and poor scalability, particularly as model complexity and dataset size increase. This review addresses the limitations of conventional HPO techniques by comprehensively analyzing the application of Metaheuristic Algorithms (MAs) in this domain. We formulate the HPO problem as a black-box global optimization task, clearly defining the objective function and the constrained search space. Furthermore, this paper introduces a structured taxonomy for Hybrid Metaheuristic Algorithms (HMAs), classifying them into sequential, parallel, and integrated architectures. We provide detailed reviews of foundational algorithms Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and Differential Evolution (DE) and subsequently analyze their synergistic combinations, focusing on frameworks like GA-PSO and Memetic Algorithms. To demonstrate practical efficacy, a case study involving the hyperparameter tuning of a Convolutional Neural Network (CNN) on the Canadian Intitute for Advanced Research-10 (CIFAR-10) benchmark is presented, illustrating how HMAs balance global exploration with fine-grained exploitation. This comprehensive review establishes a foundational framework for designing robust and computationally efficient HPO strategies tailored for modern Deep Learning (DL) systems, serving as a critical resource for future research.