We delved into a category of output feedback nonlinear systems that are distinguished by unmodeled dynamics, quantized input delays, and dynamic uncertainties. We introduce a novel finite-time adaptive dynamic surface control scheme developed through the construction of a first-order nonlinear filter. This approach integrates Young's inequality with neural network technologies. Then, to address unmodeled dynamics, the scheme incorporates a dynamic signal and utilizes Radial Basis Function (RBF) neural networks to approximate unknown smooth functions. Furthermore, an auxiliary function is devised to mitigate the impact of input quantization delays on the system's performance. The new controller design is both simple and effective, addressing the "hasingularity" problems typically associated with traditional finite-time controls. Theoretical analyses and simulation outcomes confirm the effectiveness of this approach, guaranteeing that all signals in the system are confined within a finite period.