Shoreline attributes and extensive field surveys of aquatic vegetation and animal presence were used to determine probabilities of Common Loon (Gavia immer) nesting for segments of lakeshore on 35 lakes in north-central Minnesota. Model development used both a general linear mixed model and random forest classifier approach. The resulting nesting habitat models were used to predict nesting sites for a small set of independent lakes. Shoreline segments with low mean fetch and littoral slope, fewer developed shoreline parcels, and higher aquatic plant richness had higher probabilities of nesting. In addition, significantly more nesting sites were on islands than on mainland shoreline segments. The locations of predicted nesting sites on the independent lake set compared favorably to the locations of observed nests. The ability to predict suitable Common Loon nesting sites should lead to the greater protection or restoration of these valuable areas and enhance conservation efforts across the state.