Modern civilization depends more and more on electricity, which causes a gradual expansion in the importance and use of power infrastructure. Concurrently, the modalities of investment and distribution are changing from massively centralised electricity generation and pure consumption to highly sophisticated clients and decentralised generators. This change puts additional burden on the ageing infrastructure, requiring large outlays in the coming years to guarantee a steady supply. While reducing the likelihood of problems, subsequent technical and prediction technologies can help to optimise the utilisation of the current grid. Some of the local grid challenges are covered in this paper along with a potential maintenance and failure probabilistic model. A high Volta safeguards and maintains under fault conditions to give consumers an efficient and convenient power source. Real and reactive power converter observations of electronic values are the foundation of the majority of fault localization and identification techniques. Metrics and on-the-ground analyses based on internet traffic demonstrate this. The methods for error localization, diagnosis, and detection in overhead lines are thoroughly examined in this work. The proposal can then include recommendations for methods that could be used to anticipate anticipated electrical network failures. While SVM and logistic regression produce findings with reasonable accuracy, the three classifiers—Random Forest, XGBoost, and Decision Tree—produce results with excellent accuracies..