This research examines the dynamic and efficient allocation of radio spectrum in fifth generation (5G) mobile networks using bio-inspired artificial intelligence algorithms, such as Ant Colony Optimization (ACO) and Artificial Bee Colony (ABC). The study responds to the increasing demand for spectral resources caused by the proliferation of connected devices and the stringent requirements for low latency and high reliability in 5G environments. To this end, experimental methodologies and MATLAB simulations were applied, evaluating performance under scenarios of high user density and traffic load variability. Key indicators such as spectral efficiency, throughput, latency, jitter, fairness index and signal-to-noise ratio (SNR) were analyzed. The results obtained show that bio-inspired algorithms offer substantial improvements in terms of adaptability and spectrum utilization, especially in dynamic contexts and with high probability of interference. However, challenges remain, such as high computational complexity and the need to make decisions in real time.