An artificial neural network (ANN) model was applied to simulate the phase volume fraction of titanium alloy under isothermal and non-isothermal hot forging condition. For isothermal hot forging process, equilibrium phase volume fraction at specific temperature was predicted. For this purpose, chemical composition of six alloy elements (i.e. Al, V, Fe, O, N, and C) and specimen temperature were chosen as input parameter. After that, phase volume fraction under non-isothermal condition was simulated again. Input parameters consist of initial phase volume fraction, equilibrium phase volume fraction at specific temperature, cooling rate, and temperature. The ANN model was coupled with the FE simulation in order to predict the variation of phase volume fraction during non-isothermal forging. Ti-6Al-4V alloy was forged under isothermal and non-isothermal condition and then, the resulting microstructures were compared with simulated data.