The ability to classify the biological condition of unsurveyed streams accurately would be an asset to the conservation and management of streams. We compared the ability of 5 modeling methods (classification and regression trees, conditional inference trees, random forests [RF], conditional random forests [cRF], and ordinal logistic regression) to predict stream biological condition (very poor, poor, fair, or good) based on benthic macroinvertebrate Index of Biotic Integrity data taken from the Maryland Biological Stream Survey. Predictor variables included land use and land cover (e.g., impervious surface, row-crop agriculture, and population density) and landscape measures (annual precipitation and watershed area). We included 1561 sites on small nontidal streams in the Maryland portion of the Chesapeake Bay watershed. We used 1248 sites (80%) as a training data set to build models and 313 sites (20%) as an independent evaluation data set. RF and cRF models most accurately predicted observed integrity scores in the evaluation data set, but we selected the cRF as the best model because of weaknesses in the RF model (e.g., biased variable selection). Percent impervious surface was the most important variable in the cRF model, and the probability that a site was in very poor or poor biological condition increased rapidly as % impervious cover increased up to 20%. When applied to predict stream biological conditions in all 7908 small nontidal stream reaches in the study area, the cRF model predicted that 33.8% were in fair, 29.9% in good, 22.7% in poor, and 13.6% in very poor biological condition. Our analyses can be used to manage and conserve freshwater and estuarine resources of Maryland and the Chesapeake Bay watershed. Model predictions for unsurveyed streams can help target field studies to identify high-quality streams deserving of conservation and impaired streams in need of restoration.