The machine learning model developed in this study demonstrated strong performance in prospective validation across a diverse set of HPLC traces, achieving an accuracy of 0.96 and an F1 score of 0.92 in detecting HPLC traces affected by air bubble contamination, formulated as a binary classification problem. Furthermore, we provided a proof-of-principle demonstration of repurposing the ML model for validation of HPLC instruments based on systematic performance evaluation over a large set of experiments, which appeared to have higher sensitivity compared to individual control experiments.