<p>This paper presents a predefined-time adaptive control scheme for a class of strict-feedback nonlinear systems affected by input delay and backlash-like hysteresis. To handle unknown nonlinearities, radial basis function neural networks (RBFNNs) are utilized for function approximation. The challenge posed by input delay is addressed through the use of Pade approximation. Furthermore, the command filter technique is incorporated into the control design to mitigate the issue of complexity growth commonly encountered in backstepping approaches. To improve system stability and mitigate the effects of filtering inaccuracies, a predefined-time compensation mechanism for the error is introduced. This control scheme ensures that all closed-loop signals stay within bounded limits and that the tracking error is driven into a small neighborhood around the origin within a specified finite time. The effectiveness and robustness of the proposed control strategy are demonstrated through simulation results.</p>