High reliability, high speed and low cost are the prime factors account for the complexity of electronic systems. Reliability and failure prediction are the major constraints to estimate the residual life of the component to anticipate the costly failures or system unavailability. Reliability prediction of passive components, especially PN junction diode, is of great concern as it is a critical element of bipolar junction transistors and other semiconductor devices, so the chances of failure as well as damage are increased as every component has its own characteristics and operating conditions. In this paper, artificial Intelligence techniques are employed on PN junction diode which embrace knowledge of failure mechanism of a component and predict the residual life of the component and a preventive action to be taken before serious breakdown occurs. The residual life calculated from experimental method is compared with artificial intelligence techniques, namely. ANN, fuzzy logic and ANFIS. The ANFIS has been proved as the most accurate system to predict remaining useful lifetime with an accuracy of 99.03%. A Graphical user interface is also designed based on fuzzy inference system, which indicates the remaining useful lifetime of PN junction diode.