The production of electricity in any society is an essential tool and symbol of development for such a community. In the past, the generation of electrical power has primarily relied on fossil fuels, such as coal, which emit CO2 into the atmosphere, thereby depleting the ozone layer and leaving our planet at risk of destruction. In today's world, efforts are being made to ensure the generation of electrical power through carbon-free generators. Regional Transmission Operators (RTOs) and Independent System Operators (ISOs) have set targets to ensure carbon-free generation in the near future. Owing to this development, there is an increasing addition of renewable generators to the grid, and a future of a 100% renewable generation mix is envisaged. The integration of renewables into the existing grid is also subject to changes in power flow within the power system, which may lead to surges in voltage or current, potentially causing electrical faults in the transmission line. In this research, the behavior and operation of distance relays are simulated, and a protective dataset is created using artificial intelligence methods to predict faults in the line in a 100% clean electricity. This study explores the application of machine learning models in predicting fault conditions in transmission lines based on resistance, inductance, impedance, power, voltage, and current values of the electrical power supply. Three models—logistic regress (Korstanje, sept 2022)ion, support vector machine (SVM), and K-nearest neighbors (KNN)[6]—were trained and evaluated using a dataset emphasizing the power flow results of the transmission line model derived from the Matpower. Hyperparameter tuning was performed to improve predictive accuracy, with SVM [5] (W.Urooj, 2021)and logistic regression showing superior performance in using indices such as current, voltage, and impedance to determine any presumed line faults.