In this article, a fixed-time fault-tolerant trajectory tracking control based on bias neural network is proposed for robotic manipulators with input saturation and actuator faults. Firstly, a dynamic model of the multi-joint robotic manipulators is developed, incorporating input saturation and actuator faults. Subsequently, a method to compensate for input saturation is devised, aimed at achieving control input compensation within a fixed-time frame. Following that, a fixed-time fault-tolerant control strategy is introduced, utilizing a bias neural network to approximate disturbances and the total faults of the actuators. Finally, The Lyapunov theory is employed to demonstrate the global fixed-time convergence of the system. Simulation results are conducted to validate the robustness and rapid convergence of the proposed control method within a specified time frame.