The Internet of Things (IoT) has recently become popular for collecting and storing data in third-party datasets. When combined with IoT devices, fog computing (FC) efficiently manages large data volumes and processing demands. However, concerns persist regarding privacy, edge node latency, data security, and energy consumption. With the increasing automation in smart cities, the workload for fog nodes (FNs) is developing, and additional FNs are needed. The optimal allocation of resources is essential in addressing the resource allocation (RA) issues in executing IoT applications within FC. To tackle this, the mixed integer linear Ant Lion optimizer (MILALO) model has been deployed to optimize resource allocation, reduce execution time, and conserve energy in fog computing. The proposed model overcomes challenges by optimizing resource allocation, reducing execution time, and conserving energy in fog computing. It targets efficient resource utilization and enhances scheduling, optimization, and cloud resource management to improve overall time and energy consumption. This model mediates between the network and users to process and present results by constructing an allocation matrix for the allocator. Simulations confirm the effectiveness of the MILALO model, with demonstrated 20-25% cloud optimization improvement and 50-60% reduction in time and energy consumption. It conducts a thorough assessment of the proposed model's effectiveness through key performance indicators such as execution time (ET), energy consumption (EC), and resource utilization (RU). Finally, a detailed comparative analysis against established techniques enriches the discussion, providing valuable insights into the superiority of the proposed technique.