Power system reliability and safety depend on transmission line problem detection. Wavelet transform, a sophisticated signal processing technique that analyzes signals time–frequency, is used to identify transmission line faults in this abstract. A preprocessing voltage or current signals from the transmission line removes noise and undesirable components. Wavelet transforms signal into frequency components over time to provide a time–frequency representation. Fault characteristics are taken from altered signals. Thresholding separates normal from abnormal circumstances. Faults are indicated by extracted characteristics exceeding criteria. Comparing wavelet transform coefficients at multiple transmission line points helps pinpoint the issue. The suggested method’s efficacy relies on wavelet function, feature extraction, and threshold setting. To identify faults accurately, experimental validation and optimization are needed. Wavelet transform might improve transmission line fault detection and power system stability. Power system protection has focused a lot of attention on developing new ways for identifying the kind and precise location of problems in the power system. Fault localization is one of the key features that may boost a new protection relay’s effectiveness in a power system. The wavelet transforms and neural networks are employed in this article to locate faults in transmission lines. The authors of the current study have created an algorithm for finding eleven different fault kinds across a 100% of line length. The fault location is determined via wavelet transform-extracted characteristics of voltage and current data being fed into an artificial neural network. The pragmatic challenge posed by multiple estimating was taken into consideration while developing the method.