This paper introduces an innovative application of an Artificial Neural Network (ANN) based model for the performance prediction of a power generation gas turbine. this approach optimizes the ANN model by utilizing a comprehensive database to compare various ANN topologies. Based on optimization results, a two-layer Multi-Layer Perceptron (MLP) was constructed and used as the best-optimized topology for such applications. The training dataset comprises historical operational data from a Rolls-Royce (RB21-24G) gas turbine unit. Notably, this model shows substantial accuracy for different ambient conditions and variable power ratings. Furthermore, a sensitivity analysis using various methods was introduced to study the impact of each input on the model outputs. To validate the model's reliability and novelty, we introduce a degradation study, comparing one-year-later on-site operational data with predicted values generated by the ANN model. Remarkably, the results demonstrate strong consistency between measured data and model predictions. © 2024 AIUB Office of Research and Publication. All rights reserved.