To enable the retrieval of nearshore lake-water turbidity using spaceborne LiDAR data， this study processed ICESat-2 data to extract photon-distribution characteristics over lake surfaces. Leveraging the observed variation in photon-distribution patterns under different turbidity conditions， turbidity levels were inferred accordingly. Lake Erie， one of the North American Great Lakes， was selected as the study area. An adaptive-parameter pruned quadtree algorithm was employed to denoise the ATL03 photon data from ICESat-2， isolating valid water-surface photon returns. Key photon features-penetration depth， photon density， and attenuation rate-were extracted from the processed data and matched with in situ turbidity measurements. A turbidity-retrieval model was then developed using machine-learning regression algorithms. Experimental results demonstrate that the Random Forest algorithm yields the best performance， achieving a coefficient of determination （R²） of 0.91， a mean absolute error （MAE） of 1.66 NTU， and a root mean square error （RMSE） of 2.17 NTU， indicating high retrieval accuracy within the 0-50 NTU turbidity range.To further assess the method’s applicability under different turbidity conditions， the dataset is divided into low-to-moderate turbidity （0-30 NTU） and high turbidity （>30 NTU） subsets. Results show that retrieval accuracy is slightly higher for the low-to-moderate turbidity group. This study provides a novel technical approach for remote sensing-based monitoring of lake water turbidity.