Earthquakes over the world cause serious damages for people and the economy. The establishment of an appropriate model to estimate potential losses or injuries in earthquake disasters is fundamental to decrease their impacts and losses and effectively respond and mitigate. Turkey as a country that experienced many earthquakes in the last century and had serious human and financial losses needs a comprehensive knowledge of consequences of devastating earthquakes to be able to plan for the future. Artificial neural networks (ANNs) have abilities to solve and analyse complex relations as an appropriate method to estimate number of injuries. In this study, an ANN model is built up for the earthquake casualty prediction, which takes earthquake occurrence time, earthquake magnitude, and population density as the predictors and employs 21 Mw > 5 earthquake disasters occurred in Turkey from 1975 as samples for the training of the network. The model was then tested on a study region consisting of four districts in Istanbul which is estimated to have the highest injury rate according to the earlier reports and generated estimations of the expected number of injured people. Results show that 99.9 % of the variability in the number of injured people is predictable with using this ANN model. Comparison of actual values and estimated output values in the ANN model was also found apparently very close to each other. According to the test case study results, when the value of earthquake magnitude is 6.5 Mw, number of injured people has increased in a sharp trend compared to the previous magnitude value (6 Mw) for all districts. Estimated number of injured people in daytime is obtained higher than at night when the earthquake magnitude is 5 and 5.5 Mw. The highest value in estimated number of injured people has emerged in Fatih district as 7241 for a 7.5 Mw daytime earthquake. On conclusion, it was deduced that the model can reveal accurate estimation of casualties and it can provide information in order to develop mitigation policies, especially in earthquake emergency service management.