Accurately predicting the creep failure life of adhesive joints, particularly single-lap adhesive joints (SLAJs), remains still a significant challenge, requiring substantial time and resources and the ability to predict the duration of creep failure in SLAJs is critical to ensuring structural integrity and reducing the failure of creep-prone adhesive joints. In this study, machine learning (ML) was used to identify the critical features that ultimately influence the durability of SLAJs due to creep. These key features were determined through correlation analysis and sequential feature selection. Multiple ML algorithms were employed to analyze complex relationships among key features and predict creep failure life. Finally, the results of the analysis highlight the importance of features such as SLAJ creep strain, adhesive tensile strength (UTS), SLAJ creep stress, adhesive surface area (A), and Young's modulus (E). Of the ML models tested, the random forest (RF) model was the most effective in predicting creep failure life. Moreover, the accuracy of the predictions made by the proposed ML model, using original code written in Python, has been verified in experimental tests. All datasets generated and analyzed during the current study, along with the code, are available in the repository accompanying the paper.