This study employed an artificial neural network for population prediction (ANNPP) that handles
incomplete and inconsistent nature of data usually experienced in the use of mathematical and
demographic models while carrying out population prediction. ANNPP uses the three demographic
variables of fertility, mortality and migration which are the major dynamics of population change as the
input data. The datasets were divided into train, validation and test data. The train data was presented
to the supervised artificial network to approximate some known twelve target values of population
growth rates. The method was also used to simulate both the validation and the test datasets as case
data on the consistency of results obtained from the training session via the train data. From the
sixteen different topologies tested on the basis of the mean square errors (MSE), standard deviation
(STDEV) and epochs; topology 19-9-1 performed best than the rest. A comparison between the
predictions based on the ANNPP derived growth rates and The cohort component method of population
prediction (CCMPP) was compared. The results showed that ANNPP percentage accuracies ranged
between 81.02 and 99.15% while that of CCMPP percentage accuracies ranged between 64.55 and
86.43%. These results showed that artificial neural network model performed better than the
demographic model.