This review provides a comprehensive survey of non-invasive techniques for muscle fatigue monitoring. Muscle fatigue is a complex physiological and psychological phenomenon that impairs physical performance and increases injury risk. Continuous monitoring is essential for early detection and management. Non-invasive techniques such as electromyogram (EMG), mechanomyogram (MMG), near-infrared spectroscopy (NIRS), ultrasound (US), and surface EMG (sEMG) are commonly used for muscle fatigue detection. EMG records muscle electrical activity, MMG records muscle fiber vibrations, NIRS measures muscle oxygen levels, and US detects muscle deformation signals. Each technique has specific principles, parameters, applications, and advantages/disadvantages. EMG is a mature, non-invasive method for muscle fatigue monitoring. It measures muscle activation through electrical signals. EMG signals are affected by factors such as muscle fiber type, electrode placement, and external noise. Preprocessing steps like filtering, segmentation, and rectification are essential for accurate analysis. Time-domain features like RMS and integrated EMG (IEMG) and frequency-domain features like Mean Power Frequency (MPF) and Median Frequency (MDF) are used to detect fatigue. Studies show that IEMG increases with fatigue progression, while MPF and MDF decrease. These features are sensitive to muscle fiber recruitment and synchronization. MMG records muscle fiber vibrations and is sensitive to mechanical properties of muscle fibers. It is less affected by external noise and can provide information about muscle fiber mechanics. MMG features include time-domain (RMS) and frequency-domain (MPF, MDF) parameters. MMG is useful for monitoring muscle fatigue in isometric contractions, where it shows changes in amplitude and frequency during fatigue. The review also discusses the limitations of these techniques, including the need for real-time monitoring and the complexity of dynamic contractions. Future research should focus on improving the accuracy and reliability of these methods for clinical and real-world applications.This review provides a comprehensive survey of non-invasive techniques for muscle fatigue monitoring. Muscle fatigue is a complex physiological and psychological phenomenon that impairs physical performance and increases injury risk. Continuous monitoring is essential for early detection and management. Non-invasive techniques such as electromyogram (EMG), mechanomyogram (MMG), near-infrared spectroscopy (NIRS), ultrasound (US), and surface EMG (sEMG) are commonly used for muscle fatigue detection. EMG records muscle electrical activity, MMG records muscle fiber vibrations, NIRS measures muscle oxygen levels, and US detects muscle deformation signals. Each technique has specific principles, parameters, applications, and advantages/disadvantages. EMG is a mature, non-invasive method for muscle fatigue monitoring. It measures muscle activation through electrical signals. EMG signals are affected by factors such as muscle fiber type, electrode placement, and external noise. Preprocessing steps like filtering, segmentation, and rectification are essential for accurate analysis. Time-domain features like RMS and integrated EMG (IEMG) and frequency-domain features like Mean Power Frequency (MPF) and Median Frequency (MDF) are used to detect fatigue. Studies show that IEMG increases with fatigue progression, while MPF and MDF decrease. These features are sensitive to muscle fiber recruitment and synchronization. MMG records muscle fiber vibrations and is sensitive to mechanical properties of muscle fibers. It is less affected by external noise and can provide information about muscle fiber mechanics. MMG features include time-domain (RMS) and frequency-domain (MPF, MDF) parameters. MMG is useful for monitoring muscle fatigue in isometric contractions, where it shows changes in amplitude and frequency during fatigue. The review also discusses the limitations of these techniques, including the need for real-time monitoring and the complexity of dynamic contractions. Future research should focus on improving the accuracy and reliability of these methods for clinical and real-world applications.