Accurately predicting the creep failure life of bonded joints, particularly single-lap adhesive joints (SLAJs), remains a significant challenge, requiring considerable time and resources. The ability to predict the duration of creep failure in SLAJs is crucial for ensuring the structural integrity of structures and limiting the failure of creep-prone adhesive connections. This study utilizes machine learning (ML) to identify critical features and predict the creep failure life of adhesive-bonded single-lap joints. Significant features influencing the behavior of SLAJ-type connections were identified through correlation analysis and sequential feature selection. Multiple ML algorithms were employed to analyze complex relationships among key features and predict creep failure life. The results highlight the importance of factors such as creep strain of SLAJ, Ultimate Tensile Strength of adhesive (UTS), creep stress of SLAJ, area of adhesive (A), and Young's modulus (E) in reliably predicting creep failure life. The Random Forest (RF) ensemble ML model, incorporating these features, demonstrates reliable predictive accuracy. In conclusion, the developed ML model exhibits significant potential for accurately predicting the creep failure life of adhesive-bonded SLAJs. Finally, the ML model was validated using experimental studies. The datasets generated and analysed during the current study are available in the repository attached to the paper.