Early determination of muscle fatigue helps prevent muscles from getting permanently damaged. In this work, the muscle fatigue condition is assessed from the dataset of electromyography (EMG) signals acquired during isometric contraction activity of the lower extremity muscles of limbs from eight different locations. The EMG signals are analyzed using discrete wavelet transform (DWT) and decomposed using ten different mother wavelets up to seven levels. As the muscle fatigue is directly related to the power of the signal, the power of decomposed EMG signals is calculated for all seven levels. Analysis of variance (ANOVA) test is employed to identify statistically significant differences between the non-fatigue (up-task) and fatigue groups, utilizing a threshold value of p < 0.1 to highlight noteworthy trends in power levels across various frequency bands using DWT. The results revealed that the right vastus lateralis (RVL) muscles exhibited significant changes in power levels across multiple wavelet decomposition levels, particularly in the frequency ranges of 500–1000 Hz, 250–500 Hz, and 15–30 Hz. Among the ten mother wavelets used in the analysis, the wavelet Symlet 5 in the frequency range of 250–500 Hz range demonstrated the highest sensitivity to fatigue, suggesting its potential utility in fatigue analysis. The findings indicate that EMG power levels increase under fatigue conditions, providing valuable insights for assessing muscle performance during isometric contractions.