<p>This study introduces a novel iterative algorithm for addressing the Split Common Fixed Point Problem (SCFPP) involving two nonlinear mappings defined on uniformly convex and uniformly smooth Banach spaces. The proposed method integrates a self-adaptive dynamic step-size rule, which is updated automatically at each iteration based on simple computations, thereby eliminating the need for any prior estimation of the operator norm—a common limitation in many existing approaches. We establish a strong convergence theorem ensuring that the generated sequence converges to a common fixed point that simultaneously satisfies the associated split structure. Furthermore, we demonstrate that the proposed framework naturally extends to a broader class of problems, particularly the split inclusion problem, highlighting the generality of our results. To validate the theoretical findings, we present a series of numerical experiments on benchmark tasks such as signal recovery and image restoration, which confirm the algorithm’s accuracy, robustness, and computational efficiency compared to conventional methods. These findings underline the potential of the proposed approach for practical applications in optimization, inverse problems, and applied analysis.</p>