This paper addresses the problem of model-free control and sensor fault detection for discrete-time nonlinear systems by proposing an intelligent proportional–integral–derivative controller in conjunction with a fractional-order sliding mode observer. The proposed method eliminates the need for an explicit mathematical model and relies solely on input and output signals. By integrating sliding mode observation with fractional calculus, the proposed fault detection scheme benefits from enhanced memory characteristics and increased degrees of freedom in the observer design. This integration enables an effective trade-off between detection accuracy and convergence speed, thereby improving the overall performance of the fault detection mechanism. Enhancing the fault detection mechanism significantly reduces false alarm rates, thereby improving operational reliability and yielding tangible benefits in terms of economic efficiency. To intelligently tune the controller and observer parameters, a Reinforcement Learning–based optimization strategy is employed. This learning mechanism enables the adaptive tuning of controller and observer design parameters to achieve enhanced tracking accuracy while simultaneously improving the precision of residual generation for reliable fault detection within the system. The stability of the resulting closed-loop system is rigorously established using Lyapunov theory. The effectiveness and superiority of the proposed approach are validated through comprehensive simulation studies conducted on a data-driven, model-free nonlinear discrete-time system. The simulation results demonstrate significant improvements in tracking performance, robustness, and fault estimation accuracy compared to conventional approaches.