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Transmission lines serve as essential lifelines in modern electrical power systems, facilitating the seamless transfer of electric energy across vast geographical distances. These lines are continuously subjected to a variety of stressors, including environmental extremes, equipment deterioration, and operational overloads, which can lead to different types of faults. Effective and prompt fault detection, classification, and location are imperative for preserving grid reliability, minimizing equipment damage, and reducing system downtime. This paper presents a holistic review of the state-of-the-art transmission line fault diagnosis methods. We discuss classical techniques such as impedance-based analysis and traveling wave phenomena, and juxtapose them with intelligent paradigms grounded in machine learning, deep learning, and hybrid computational models. The integration of Phasor Measurement Units (PMUs), advanced communication protocols, and the emergence of Internet of Things (IoT)-enabled frameworks in smart grid settings are critically analyzed. Moreover, we identify major research trends including edge computing for low-latency diagnostics, cybersecurity for protection systems, and the deployment of digital twins for predictive analytics. Through a structured comparison, this review delineates the performance trade-offs of existing techniques and underscores future directions to enhance the intelligence, responsiveness, and resilience of power system protection schemes.