Air-traffic optimization is an essential part of airspace operation reengineering, as the number of flights and the usage of routes increase in the world. The NextGen and SESAR projects are important initiatives that allow for more scalability and safety in air-traffic. One element of these projects is the Automatic Dependent Surveillance-Broadcast (ADS-B) system which allows airplanes to share their position and speed. The ADS-B antennas’ coverage is somewhat limited in less economically developed and oceanic areas, resulting in a lack of flight path data. This paper proposes a method based on artificial neural networks (ANN), interpolation and average computation to fill flight path data partially tracked by ADS-B antennas. While other methods are focused on one or two dimensions of the flight path, this work is focused on infilling the 4-dimensions present in the ADS-B data (latitude, longitude, altitude and ground speed). This work is useful in analyzing performance of historical flights related to limited coverage areas or in predicting flight path in air-traffic management systems (ATMs). The comparison between the real and estimated trajectories in a set of 517 flights has shown accuracy superior to 92% for the metrics distance flown, estimated burned fuel, and trajectory correlation.