Reliable fault detection, classification, protection, and location are essential for secure operation of modern HVAC and HVDC transmission networks. This task has become more complex due to long EHV corridors, DFIG-based wind farms, converter-interfaced renewable sources, LCC and MMC-HVDC links, series-compensated lines, and hybrid AC–DC systems. These networks may produce limited fault current, non-stationary transients, converter-controlled responses, frequency-dependent travelling-wave propagation, and reflection effects at converter or cable–overhead interfaces. Consequently, conventional overcurrent, impedance, distance, and differential protection schemes may lose sensitivity and accuracy under high-resistance faults, weak infeed, load encroachment, CT saturation, close-in faults, and converter-dominated conditions. This review analyses classical protection, travelling-wave methods, DWT, CWT, WTMM, WPT, S-transform, HHT, decaying DC methods, and intelligent classifiers including ANN, BPNN, SVM, decision tree, random forest, LSTM, RNN, and GCN. The study shows that travelling-wave and wavelet-based methods offer high-speed detection and accurate location, while intelligent methods improve classification when trained with diverse fault data. The review concludes that hybrid protection combining transient features, travelling-wave location, and machine-learning-assisted classification is a promising direction for practical transmission-line protection.