Contemporary technologies, such as the Internet of Things (IoT), facilitate real-time access to information, influencing decision-making processes in smart cities and industries. Data is transmitted to cloud storage systems daily, undergoing analysis and processing within the network. Implementing Fog Computing (FC) technology introduces inherent challenges, particularly in managing energy consumption and enhancing response speed. This paper proposes a novel hybridization of Crow Search and Non-Monopolize Search (CSA-NMS) to address the task scheduling problem in FC, aiming to enhance the quality of services supplied by IoT devices. Recent metaheuristic algorithms were examined to identify potential solutions to the energy consumption problem. A comparison of various algorithms, such as the Genetic Algorithm (GA), Flower Pollination Algorithm (FPA), Particle Swarm Optimization Algorithm (PSO), Bat Algorithm (BAT), Crow Search Algorithm (CSA), and the amended version proposed in this study, the Crow Search en synergy Non-Monopolize Search (CSA-NMS), is presented in this work. The study’s findings indicate the superiority of the (CSA-NMS) compared to other analyzed methods. This discovery suggests a more efficient alternative for addressing the energy challenges associated with fog computing.