The criticality of maintaining a consistent electricity supply and safeguarding electrical infrastructure hinges on the dependability and stability of power distribution systems. Fault detection and classification are essential elements of power system protection, facilitating the early recognition and mitigation of faults to minimize service interruptions and equipment harm. This investigation introduces a sophisticated methodology that merges Discrete Wavelet Transform (DWT) and Support Vector Machine (SVM) strategies to elevate the accuracy and efficiency of fault classification and detection in power distribution systems. The DWT is utilized for fault signal processing, converting them into time–frequency domain representations that capture transient characteristics and localized features crucial for precise fault identification. These characteristics are subsequently inputted into the SVM, a machine learning algorithm recognized for its superior classification accuracy. The suggested method is evaluated using simulated fault data under different conditions, showcasing exceptional detection accuracy, classification precision, and computational efficiency. This research underscores the potential of integrating advanced signal processing and machine learning techniques to improve fault detection and classification in power distribution systems, guaranteeing a more reliable and effective electricity supply. The technique is tested using a machine learning platform called WEKA.