We developed PeatDepth-ML, a machine learning model predicting peat depth worldwide to help estimate carbon stocks in these climate-critical ecosystems. Our model predicts median depths of 134 cm in peatlands. Using bootstrapping, we rigorously assessed how sampling bias affects predictions. This revealed predictor selection and regional accuracy can vary greatly with different data subsets, demonstrating model reliability fundamentally depends on training data quality and geographic coverage.