An ANN-based intelligent overcurrent relay is proposed for simultaneous fault detection and fault-type classification in a selected subsection of the IEEE-9 bus transmission system (Bus-7–Bus-8). Two neural network modules are implemented: the first performs fault detection and directly issues the trip command based on three-phase current features, while the second classifies the fault types into AG, BG, CG, AB, BC, CA, and ABC categories. Fault current signals are generated in MATLAB/Simulink under diverse operating conditions, including variations in fault resistance, location, and inception angle. The detection network achieved a correlation coefficient of R ≈ 0.993, whereas the improved classification network achieved R ≈ 0.998, demonstrating a substantial enhancement in accuracy and generalization. Time-domain tests demonstrated consistently faster tripping performance compared with the conventional inverse-time relay namely, improvements of 0.6 ms (AG), 0.7 ms (BG), 1.25 ms (CG), 0.6 ms (AB), 1.5 ms (BC/BC-G), 1.6 ms (AC/AC-G), and 0.5 ms (ABC/ABC-G). These results confirm the superior dynamic response and adaptability of the proposed intelligent relay, highlighting its suitability for modern protection applications and its potential as a foundation for next-generation smart-grid relaying systems.