This paper investigates the adaptive neural network tracking control problem for a class of nonlinear systems subject to external disturbances and input saturation. Unlike existing studies that often overlook the potential conflicts between coexisting input and output constraints, a modified fixed-time prescribed performance function is constructed to achieve a dynamic balance between the two. This mechanism actively relaxes performance boundaries to circumvent singularities during actuator saturation and restores the preset performance once saturation vanishes. Furthermore, a novel finite-time command filter, complemented by an error compensation mechanism, is introduced to resolve the "explosion of complexity" inherent in the backstepping design. Lyapunov stability analysis theoretically demonstrates that all signals in the closed-loop system are semi-globally uniformly ultimately bounded (SGUUB), with the tracking error converging to a small tunable neighborhood of the origin. Simulation results are provided to validate the effectiveness of the proposed strategy.