High-resolution elevation data provide a foundational layer needed to understand regional hydrology and ecology under contemporary and future-predicted conditions with accelerated sea-level rise. While the development of digital elevation models (DEMs) from light detection and ranging data has enhanced the ability to observe elevation in coastal zones, the elevation error can be substantial in densely vegetated coastal wetlands. In response, we developed a machine learning model to reduce vertical error in coastal wetlands for a 1-m DEM from 2018 that covered Nassau and Duval Counties, Florida. Error was reduced by using a random forest regression model within situ observations and predictor variables from optical and radar-based satellite data and elevation derivatives. Vegetation and elevation data were collected using a real-time kinematic global positioning system (RTK GPS) in coastal wetlands at the National Park Service’s Timucuan Ecological and Historic Preserve in summer 2021 and winter 2022 (n = 344). Predictor variables included information on vegetation greenness, wetness, elevation, and vegetation structure. In the extent of coastal wetlands in Nassau and Duval Counties, the original DEM had a mean absolute error of 0.17-m and a 95th percentile error of 0.48 m. Leave-one-out cross-validation was used to assess the accuracy of the corrected DEM. In coastal wetlands, the corrected DEM had a mean absolute error of 0.08 cm and a 95th percentile error of 0.25 m. The random forest model led to a decrease in the mean absolute error by about 50% and a decrease in 95th percentile by 49%.