The ubiquity nature of mobile networks, growth in technology, and innovations in mobile servicesrnwill attract more users with the growing expectation and satisfaction. To accommodate thernincreasing number of usersâ€™, operators are investing more in infrastructure and service delivery. Inrnorder to get the right revenue with the appropriate investment, the network planning andrnoptimization work has to be done properly. In this regard, propagation pathloss is one of the mainrninputs for the planning and optimization and it has to be predicted as accurate as possible. rnPrediction of the propagation pathloss can be done using different models. These models arerndeterministic, empirical, and statistical models. From these models, the empirical pathloss models,rnsuch as, COST-231, ECC-33, Stanford University Interim (SUI) are more commonly used.rnDifferent environments own different model and accuracy. These models have got differentrnaccuracy and they are modeled for different environment. When they are applied for thernenvironment other than the area they are modeled for, they lose their accuracy. Hence, searchingrnfor a better prediction model is essential. To this end, neural network-based model is one of thernbetter solutions to empirical and deterministic models for predicting the propagation pathloss. rnThe dataset is collected from measurements through a drive-test and from low level designrndocuments. Then, it is preprocessed before used in order to train and evaluate the network. Thernperformance evaluation is done with metrics, such as Mean Absolute Error (MAE), Mean AbsoluternPercentage Error (MAPE), Root Mean Square Error (RMSE), and the coefficient of determinationrn(Rrn2rn) and Regression coefficient (R). The result of the tthesis shows that the neural network-basedrnmodel has improved the pathloss by 6.2 dBm, 4.85%, 8.98 dBm, 0.44 and 0.53. The achievedrnresult for the empirical models considering values of MAE, MAPE, RMSE, R and Rrn are 10.57rndBm, 8.34%, 14.36 dBm, 0.38 and 0.14 respectively. On the other hand, the proposed approachrnachieved a value of 4,37 dBm, 3.49%, 5.38 dBm, 0.82 and 0.67 using the test dataset. To this end,rnthe neural network-based model best fits the pathloss for the Addis Ababa city realistic casernscenario.