Distribution systems increasingly use static synchronous compensators (DSTATCOM) to reduce power quality problems and boost voltage support by compensating for reactive power. This paper deals with multi-machine nine-bus systems that use a shunt compensator to reimburse for reactive power and have a flat voltage profile under various circumstances. The further study focuses on fault analysis with discrete wavelet transforms (DWT) and radial basis function neural networks (RBFNNs). The Daubechies mother wavelet (Db4) at level 1 is used to deconstruct and extract the fault current signals to detect, diagnose, and classify a variety of defective circumstances. The fault threshold (FTH) and fault index (FI) were evaluated to distinguish between normal and fault conditions. The fault threshold (FTH) and fault index (FI) were assessed to differentiate between normal and fault circumstances. It is examined further through the RBFNN technique to distinguish between normal and anomalous occurrences during various fault situations. To identify and classify the various fault states at various busses, the RBFNN is trained and evaluated using the DWT coefficients. To determine which technique is best for fault analysis, the RBFNN technique has also been compared against feedforward neural networks (FFNN) and cascade-forward backpropagation neural networks (CFBPNN). A simulation analysis using various operating conditions is conducted in MATLAB Simulink to verify the algorithm’s efficacy. This method is validated on the dataset, and promising results are obtained. The recommended approach can accurately, swiftly, and consistently detect a wide range of system states.