The purpose of this quantitative correlational-predictive study was to determine if, and to what extent, self-talk (self-reinforcing, self-managing, combined and individually) predicts academic burnout for undergraduate students in the Southwest region of the United States. The triadic model of self-regulated learning and job demands-resource theory of burnout provided the theoretical foundation. Two research questions separated investigating the combined effect and individual effects of variables Self-reinforcing self-talk (SRST) and Self-managing self-talk (SMST) on Academic burnout (AB). Recruitment through Amazon Mechanical Turk yielded a sample of 142 full-time undergraduate students across the Southwest United States. Primary data was collected with an online survey which contained three demographic questions, the Self-Talk Scale, and Oldenburg Burnout Inventory--Student. A multiple linear regression analysis was conducted to investigate a predictive relationship. The full regression model showed self-talk (SRST and SMST combined) did not explain a significant portion of the variance of AB [F (2,139) = 0.026, p = 0.975]. SRST ([beta] = -0.027, t = -0.064, p = 0.949) and SMST ([beta] = -0.048, t = 0.132, p = 0.896) individually did not statistically significantly predict AB. The research implications for higher education administrators included an understanding of the complexity of academic burnout when implementing support programs. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://bibliotheek.ehb.be:2222/en-US/products/dissertations/individuals.shtml.]