Understanding the fatigue load history of wind turbines is critical for taking decisions regarding the lifetime of a project. However, direct measurement of fatigue loads at each turbine in a wind farm is unfeasible. For this reason, surrogate models offer a useful alternative. In this thesis, a methodology for creating surrogate models for emulating fatigue loads of offshore wind turbines is presented. The methodology is unique in that it accounts for the variability of site-specific conditions that may be present between wind turbines of the same class. First, a method for creating simplified structural models which depends only on a few degrees of freedom is derived. After this, a database of simulation data is assembled by varying the geometric, dynamic, and environmental degrees of freedom within ranges which capture a high degree of variability of possible site-specific conditions. This database is then used to train surrogate models using neural networks.