Yet it is true ideally that ATM service thought to be in service 24 hours a day, 7 days a week,rnand 365 days a year; But, frequently it has been seen on these self-service machine out ofrnservice. This study therefore aims to find the reasons for ATMs out of service, taking Dashenrnbank ATMs as a case.rnThe research follows a hybrid knowledge discovery process (KDP) model which has six steps.rnThe first step is understanding the problem, which helps to describe the problem and identifyrnattributes for data collection from purposely selected 14 ATMs within periods, December 31,rn2017 â€“ May 04, 2018. This is followed by understanding the data and prepare dataset for datarnmining tasks. Preparation of data set includes constructions of new attributes from the existingrndataset, removing unnecessary attributes and inconsistence values. From the collected dataset ofrnProView ATMs real time monitoring console as per the objective of this research 44 eventsrndescribing out of service happening were identified. The crucial step in KDP is data miningrnwhich enables to design a predictive model. In this study WEKA knowledge discovery tool isrnused to identify the reasons for ATM out of service.rnExperimental analysis shows that J48 decision tree registers the best result with accuracy ofrn52.7059 %. Finally, using the best predictive model of J48 decision tree we designed a prototypernon NetBeans IDE 8.2 platform. This prototype was evaluated based on ISO 9241-11 UT testrnfeatures by domain experts. According to the UT test the prototype was effective, efficient andrnhold on user satisfaction. However, an automatic integration of the model with ProView willrnhelp to detect and solve the problem immediately for enhancing ATM in service.