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Molding has been widely used to manufacture thermoset composite structures in the aerospace and automotive industries owing to its efficiency in reducing the number of parts and the manufacturing cost. For such molded composite parts, the degree-of-cure curve is generally used to evaluate the solidification of the resin. Nevertheless, in simulation of cure is not the cure model itself, but rather knowing the initial conditions such as fiber volume fraction, initial curing degree, convective boundary conditions etc. Additionally, solving the heat transfer coupled with the cure kinetics presents additional requirements for time, making artificial intelligence tools promising for these problems. This paper focuses on developing a data-driven approach for predicting the degree-of-cure curve. The simulated degree-of-cure curve for the model corresponds to a specific temperature–time curve was verified by the published value. Then, the temperature–time and the resulting degree-of-cure-time curves obtained from finite element simulations were created for training the prediction models using machine learning approaches of support vector regression (SVR), back propagation (BP) neural network and BP neural network optimized by genetic algorithm (GA-BP). The validation and evaluation indices illustrate that the degree-of-cure curve prediction model trained by the GA-BP neural network yields the highest accuracy.