Irregular topography can generate out-of-plane signals (OPS) on seismic sections, interfering with the imaging of the true seafloor directly beneath the survey line. While acquiring three-dimensional data or using specialized sensors can mitigate this, these options are often costly or unavailable, especially for legacy surveys. To efficiently remove OPS from two-dimensional (2D) data, this study investigates the validity of using a neural network (NN) for picking and muting. First, we demonstrate the limitation of conventional frequency–wavenumber domain directional filtering due to the kinematic similarity between OPS and true seafloor reflections. Then, we present a workflow that employs a cascade–correlation learning algorithm to identify and mute OPS arrivals before the first break. Unlike data-intensive deep learning techniques that require large training datasets, this lightweight NN is trained on user-picked examples of true seafloor reflections, enabling it to distinguish OPS events arriving from outside the vertical survey plane. Application of this technique to a 2D line acquired near irregular seafloor topography in the Ulleung Basin demonstrates the true seafloor reflector and the removal of false offline signals. Qualitative and quantitative validation against an independent external bathymetric reference both showed a reduction in travel time error compared to the raw data, confirming the effectiveness of the picking results. The results highlight that a cascade–correlation NN-based picking and muting can efficiently suppress OPS in cases of irregular topography on 2D seismic data.