A novel protection coordination approach utilizing artificial neural networks (ANNs) is introduced in this work for meshed high-voltage transmission systems. Existing overcurrent and distance relay coordination methods in meshed topologies are prone to relay blinding, zone overreach, and incorrect operation during power swing events. The developed ANN model is trained using an extensive fault scenario dataset generated through simulation of a 9-bus, 230 kV benchmark network in MATLAB/Simulink. The proposed architecture—with 18 inputs, three hidden layers containing 36, 24, and 12 neurons respectively, and a 9-output trip signal layer—delivers improved coordination speed, selectivity, and sensitivity over traditional relay configurations. Testing results demonstrate a fault classification accuracy of 98.54% on previously unseen data. On average, fault clearance times are shortened by 56.8% in comparison to conventional coordination approaches, and dependable detection of high-impedance faults is also achieved. The approach provides a flexible and adaptive protection solution well-suited to contemporary interconnected power grids.