To improve the design efficiency and performance of fiber amplifiers, this paper systematically investigates the application of artificial intelligence algorithms in fiber amplifier design, focusing on the roles of metaheuristic algorithms and neural networks in addressing three core challenges: inverse design, forward modeling, and dynamic control. The research demonstrates that metaheuristic algorithms (including genetic algorithms, particle swarm optimization, and simulated annealing) effectively optimize multidimensional parameters such as fiber length and pump configurations through natural evolution or swarm intelligence simulations. Neural networks, with their superior nonlinear modeling capabilities, enable efficient solutions for gain spectrum prediction, quality of transmission (QoT) estimation, and pulse evolution simulation, showing significant computational speed advantages over conventional numerical methods. Furthermore, the integration of metaheuristic algorithms with neural network technologies achieves adaptive real-time control in optical networks, successfully addressing the dynamic bandwidth requirements of emerging applications like video streaming and cloud computing. Finally, prospects for the application of artificial intelligence algorithms in fiber amplifiers are discussed.