Today the need for mobile communication systems and the high increase in the number of users have also made the development of new generation mobile applications indispensable. Obtaining location information has been one of the most interesting and significant areas of improvement. The purpose of the services used to determine the location is generally to obtain the information of the users such as approximate location, speed, and time. The GPS is the most preferred and globally accurate positioning system among global positioning systems. However, in addition high installation cost of the system; galactic and meteorological factors, high buildings, other physical obstacles, and especially indoor areas are some of the main constraints that can lead to serious signal degradation and losses which may cause the system to be out of service. In this context, there is an urgent need for positioning systems that will be alternative and complementary to global positioning systems. The cellular network is widely used by almost everyone and its coverage area is increasing day by day. The network has been trained and tested in the simulation environment using machine learning algorithms, namely, extreme learning machine (ELM), generalized regression neural network (GRNN), and nearest neighborhood ( NN). When compared to other cellular localization methods in the literature, the proposed system performs positioning with much higher accuracies with distance error rates below a meter (m) at minimum, and between 76-216 m on average. The test results show that it can successfully localize the mobile users with a significant accuracy for indoor, where GPS signals are very weak or cannot be received at all; and it can also stand in the breach for outdoor, where GPS may be disabled for different reasons.