Fault detection, classification, and precise location identification in power transmission lines are critical issues for energy transmission and power systems. Accurate fault diagnosis is essential for system stability and safety as it enables rapid problem resolution and minimizes interruptions in electrical energy supply. The characteristic parameters of mixed-conductor power transmission lines connected to the grid were calculated using the relevant line data. Based on these parameters, a dataset was created with computer-derived values. This dataset included variations in arc resistance and the short circuit power of the corresponding bus, facilitating the performance testing of various machine learning algorithms. It was observed that the correct determination of the faulty phase was of high importance in the correct determination of the fault position. For this reason, a gradual structure was preferred. It was achieved with a 100 percent success rate in fault detection with the ensemble bagged algorithm. It was obtained with the neural network algorithm with a 99.97 percent success rate in faulty phase detection. The most successful location results were obtained with the interaction linear algorithm with 0.0066 MAE for phase-to-phase faults and the stepwise linear algorithm with 0.0308 MAE for phase ground faults. Using the proposed algorithm, fault locations were identified with a maximum error of 26 m for phase-to-ground faults and 110 m for phase-to-phase faults on a transmission line with a mixed conductor of approximately 178 km. Additionally, we compared the training and testing results of several machine learning algorithms metrics including the accuracy, total error, mean absolute error, root mean square, and root mean square error to provide informed recommendations based on their performance. The findings aim to guide users in selecting the most effective machine learning models for predicting failures in transmission lines.