In the last two decades, an intensive effort is in being made to manufacture smart composite structures with embedded fiber optic sensors that can be exploited to detect, localize, and classify damage. In this context, the use of digital twins for the virtual design and optimization of a sensors' network may give insight to complex phenomena and significantly reduce the development time and cost of the smart structures. Composite engine fan blades are prone to bird strike damage and thus, early detection of bird strike damage is crucial for the efficient maintenance of the engines and the prevention of critical incidents. In this work, a Fiber Bragg Grating (FBG) network in the leading edge of a rotating composite fan blade subjected to bird strike is developed. The fan blade is made of 3D woven composite material and its leading edge is protected by an adhesively bonded layer made of steel while the FBG network is placed into the bondline. The Digital Twin (DT) of the above description integrates a Finite Element (FE) model and Artificial Neural Network (ANN) models. In the FE model, progressive damage in the composite material and debonding of the steel layer have been simulated. Several bird strike simulation scenarios were performed at different strike locations. At the time of the bird strike, the blade is rotating with a rotation speed of 3000 rpm. ANNs were trained using the numerical strain data in order to predict the existence of debonding of the steel layer and the area of debonding. ANNs exploit Convolutional Neural Networks (CNNs) to capture spatial dependencies in input signals. Experimental results show ANNs provide low root mean square error (RMSE) in both regression tasks, effectively predicting the bird strike location and the damage in terms of the relevant affecting area.