This study presents the design and simulation of an adaptive PID controller for the position control of a CNC linear motion table, integrating FLC and an artificial neural network (ANN) as dual-tuning mechanisms. The objective of this study is to enhance the dynamic response, accuracy, and robustness of the servo motor–ball screw drive system under machining conditions. A CNC axis mathematical model comprising the servo motor, reducer, ball screw, and load dynamics was derived to establish the nominal plant. The fuzzy logic controller provided real-time adaptive gain adjustments based on error and change of error, while the ANN, trained using the Levenberg–Marquardt algorithm, generated corrective gain increments (Δ????????, Δ????????, Δ????????) to further refine the PID parameters. The simulation results show that the proposed adaptive PID achieved a rise time of 0.0625 s, a settling time of 0.080 s according to the 2% criterion, an overshoot of 0.505%, and a negligible steady-state error. In contrast, the conventional PID from the literature has a rise time of 0.318 s, a peak time of 1.535 s, and an overshoot of 6.01%. Frequency-domain analysis further confirmed the bandwidth enhancement to approximately 10 rad/s and a phase margin of 60°, indicating enhanced stability and disturbance rejection. The adaptive tuning mechanism maintained bounded control efforts along with stable gain corrections, ensuring the practical feasibility of real-time implementation. All these results validate the fact that the proposed dual-tuner adaptive PID will provide greater precision and robustness to the control of the CNC servo motor and thus could be highly effective in current advanced manufacturing systems