Airborne bathymetric Light Detection and Ranging (LiDAR) systems have attracted attention as efficient surveying tools that acquire high-resolution and high-precision coastal topographic data more cost-effectively than traditional shipborne acoustic sounding or field surveys. Since laser pulses are refracted at the air–water interface, for the accurate registration of seafloor points, it is crucial to distinguish whether each return signal is from land or water at the waveform stage, before generating the point cloud. Conventional land–water discrimination techniques often rely on near-infrared (NIR) channel data for water-surface detection or water-body identification. However, NIR signal reliability is often compromised by specular reflection from water surfaces, and many recently developed sensors employ only a single green laser wavelength owing to system miniaturization and weight reduction. This situation underscores the necessity for land–water discrimination techniques that use only single green-channel waveform information. In this study, we analyzed various waveform features extracted from individual waveforms acquired with the Seahawk airborne bathymetric LiDAR system across coastal areas with varying water depths and turbidities. These waveforms were decomposed into Gaussian components, from which features were extracted and used in machine learning classifiers to evaluate their versatility and effectiveness for land–water discrimination under diverse coastal conditions. Four tree-based machine learning models—decision tree, random forest, XGBoost, and LightGBM—were evaluated using a stratified cross-validation scheme for performance assessment. All models achieved a high validation accuracy of approximately 0.99, demonstrating discriminative capability based on waveform features. In comparative evaluations considering both test accuracy and computational efficiency, LightGBM showed the most balanced performance, indicating its suitability as a general-purpose model for waveform-based land–water discrimination.