This paper investigates whether aircraft noise modeling can be improved by more accurately predicting the aircraft weight and thrust compared to the current methodology ECAC (European Civil Aviation Conference) described in Doc. 29 (Document 29). Using ACMS (Aircraft Condition Monitoring System) data from multiple aircraft types, two new weight estimation methods are proposed for departures: a climb slope and distance based approach, and a specific-energy method. The MAPE (Mean Average Percentage Error) of the current stage length approach is compared to the newly proposed methods. For thrust estimation, departures during the initial take-off roll and climb out are modeled using weight-dependent interpolations of the FPPs (Fixed-Point Profiles). For the other parts of the departure process, median FPPs, for which boundaries are determined by a flight segmentation model, are used. Arrival thrust values are predicted using a random forest regression model trained on flight path angle, calibrated airspeed, and corrected net thrust. This random forest model accurately captures thrust peak magnitudes and locations for most flights. Noise contour plots are generated for an original Doc. 29 model, an ACMS Doc. 29 and a new weight and thrust Doc. 29 model. For the ACMS Doc. 29 model, the ACMS thrust and weight data is directly used as input data for the noise model. The new weight and thrust estimates reveal closer agreement with ACMS Doc. 29 contours than with the original Doc. 29 method. This result indicates the rigidity of the FPPs and outdated ANP (Aircraft Noise Performance) database entries contribute to current modeling inaccuracies. The results demonstrate that the incorporation of performance relationships can significantly improve the theoretical Doc. 29 model.