The results obtained from two experimental test programs (TP-1 and TP-2) were used to train neural networks to predict elevated temperature, fatigue crack growth rates in Ti-6Al-4V alloy. Two programs, TP-1 and TP-2, were conducted at room and elevated temperatures under high humidity and laboratory air environments, respectively. While elevated temperature effects were investigated in TP-2, stress ratio effects were studied in TP-1 using several stress ratios. Networks were trained using the elevated temperature data to predict the crack growth rates at a given stress intensity under different temperatures. The experimental and predicted fatigue crack growth rates showed a least squared error of 0.03. Thus, this approach was found to predict fatigue crack growth rates in Ti-6Al-4V alloy at elevated temperatures.