Osteoporosis and related bone fractures are an increasing global burden in our ageing society. Areal bone mineral density assessed through dual energy X-ray absorptiometry (DEXA), the clinically accepted and most used method, is not sufficient to assess fracture risk individually. Finite element (FE) modelling has shown improvements in prediction of fracture risk, better than aBMD from DEXA, but is not practical for widespread clinical use. The aim of this study was to develop an adaptive neural network (ANN)-based surrogate model to predict femoral neck strains and fracture loads obtained from a previously developed population-based FE model. The surrogate model performance was assessed in simulating two loading conditions: the stance phase of gait and a fall. The surrogate model successfully predicted strains estimated by FE (r2 = 0.90–0.98 for level gait load case, r2 = 0.92–0.96 for the fall load case). Moreover, an ANN model based on three measurements obtainable in clinics (femoral neck length (level gait) or maximum femoral neck diameter (fall), femoral neck bone mass, body weight) was able to give reasonable predictions (r2 = 0.84–0.94) for all of the strain metrics and the estimated femoral neck fracture load. Overall, the surrogate model has potential for clinical applications as they are based on simple measures of geometry and bone mass which can be derived from DEXA images, accurately predicting FE model outcomes, with advantages over FE models as they are quicker and easier to perform.