In order to improve decision-making and resource allocation in cloud networks, this work explores the use of bio-inspired AI algorithms, namely Genetic Algorithms (GA), Ant Colony Optimization (ACO), and Particle Swarm Optimization (PSO). CloudSim simulations measure the algorithms’ efficiency, flexibility, and performance in dynamic cloud environments. The results show that PSO performs better on most metrics than GA and ACO. PSO completed the work in the shortest amount of time (75 s), with the maximum resource utilization (90%) and maximum energy efficiency (92%). PSO also exhibited maximum flexibility (95%) towards changing workloads and available resources. GA performed poorly regarding resource usage and flexibility, but ACO performed well, notably in energy efficiency and flexibility. The findings propose that bio-inspired algorithms, particularly PSO, could significantly improve cloud resource management by providing more efficient, flexible, and autonomous solutions for resource allocation and real-time decision-making in cloud networks.