Corrosion under insulation (CUI) has become a serious problem for piping systems especially in oil and gas industries. A recent study showed that 10 of the maintenance and repair budget is used to only spent on CUI issues in a petrochemical industry. CUI is difficult to predict since it remains hidden beneath the insulation and gets continuous insidious growth in an abrupt and uncertain manner. According to American Petroleum Institute standard i.e. API 581, operating temperature, type of insulation, pipe complexity, type of environment, and insulation condition are the main factors that causes CUI. By focusing on varying nature of CUI, a fuzzy logic-based prediction model has been developed in this study. The proposed fuzzy logic model is consists of five above mentioned key factors of CUI as input parameters while CUI corrosion rate as the output parameter in this study. Kruskal Wallis Test was applied in order to check the significance of results generated by developed fuzzy logic model. Afterwards, Sensitivity Analysis (SA) of CUI producing factors was performed for determining the percentage contribution of each individual CUI producing factor for the cause of 1 mm/year CUI in pipes. At the end of study, CUI 3D surfaces were modeled. Predicted CUI corrosion rates specifically for an operating temperature range for which API 581 has suggested zero CUI corrosion rate, SA results regarding CUI producing factors, and CUI 3D surfaces which are showing relationship between selected CUI producing factors with output CUI corrosion rate at an instant, will help inspection and corrosion engineers for monitoring and maintaining the health of pipes. Thus, it is expected that the developed fuzzy logic model will facilitate Risk Based Inspection (RBI) activities in oil and gas industries. © 2019 Elsevier Ltd