This review provides a comprehensive survey of non-invasive techniques for monitoring muscle fatigue, emphasizing their mechanisms, parameters, and applications. Muscle fatigue is a complex physiological and psychological phenomenon that impairs physical performance and increases the risk of injury. The review covers various techniques, including electromyogram (EMG), mechanomyogram (MMG), near-infrared spectroscopy (NIRS), and ultrasound (US). Each technique is detailed in terms of its principle, mechanism, and parameters used for fatigue detection. EMG, which records muscle electrical activity, is highlighted for its ability to provide real-time, continuous, and quantitative data, making it suitable for early fatigue prediction and detection. However, EMG signals are susceptible to factors unrelated to the exercise, such as contraction speed, and can be more challenging to analyze during dynamic contractions due to signal non-stationarity. MMG, which records vibration signals of muscle fibers, is noted for its sensitivity to the mechanical state of muscle fibers and neuromuscular function, offering a robust alternative in certain scenarios. The review also discusses the strengths and limitations of each technique, providing insights into their suitability for different applications in clinical diagnosis, sports science, and hardware implementation.This review provides a comprehensive survey of non-invasive techniques for monitoring muscle fatigue, emphasizing their mechanisms, parameters, and applications. Muscle fatigue is a complex physiological and psychological phenomenon that impairs physical performance and increases the risk of injury. The review covers various techniques, including electromyogram (EMG), mechanomyogram (MMG), near-infrared spectroscopy (NIRS), and ultrasound (US). Each technique is detailed in terms of its principle, mechanism, and parameters used for fatigue detection. EMG, which records muscle electrical activity, is highlighted for its ability to provide real-time, continuous, and quantitative data, making it suitable for early fatigue prediction and detection. However, EMG signals are susceptible to factors unrelated to the exercise, such as contraction speed, and can be more challenging to analyze during dynamic contractions due to signal non-stationarity. MMG, which records vibration signals of muscle fibers, is noted for its sensitivity to the mechanical state of muscle fibers and neuromuscular function, offering a robust alternative in certain scenarios. The review also discusses the strengths and limitations of each technique, providing insights into their suitability for different applications in clinical diagnosis, sports science, and hardware implementation.