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In this study, we used machine learning models to detect the goal setting and planning activities in self-regulated learning (SRL) based on the linguistic features of think-aloud transcripts. Specifically, we trained six types of machine learning models (ie, decision tree, Gradient boosted decision tree, random forest, logistic regression, support vector machine, and neural network) on 2,792 think-aloud segments of medical students, who were asked to think out loud as they diagnosed virtual patients in a computer-simulated environment. The results suggested that machine learning models, especially Gradient boosted decision tree and neural network, could make accurate predictions. This study shows the possibility of using machine learning to free researchers from the labor-intensive work of coding think-aloud transcripts. This study also informs practitioners about automatically detecting students’ SRL activities in real-time as they think aloud in learning, making the provision of timely feedback possible.