Enhancing Performance in Life Expectancy Prediction of Electronic Items using Artificial Neural Networks and comparing it with Deep Neural Networks for better accuracy. Evaluating and contrasting the performance of deep neural networks (DNNs) and artificial neural networks (ANNs) might improve the precision of life expectancy prediction for electrical equipment. In order to maximize predictive maintenance performance, it is imperative to methodically assess and improve both ANN and DNN models. Research papers, databases, and programming tools are just a few of the resources that can be used to accomplish this. A comparison research evaluated how well deep neural networks (DNNs) and artificial neural networks (ANNs) predicted how long electronic equipment will last. The findings demonstrated that DNNs outperformed ANNs in accuracy, achieving 91.2% as opposed to 88.5% for ANNs. This implies that DNNs’ more sophisticated architecture and learning powers might provide a slight edge in this prediction job. In conclusion, the accuracy of estimating the lifespan of electrical items is improved by the use of ANN and DNN algorithms. The quantity of the dataset and the required level of precision are two considerations that influence the decision between the more complex DNN and the simpler ANN.