This study aims to provide basic data to enhance the self-management competency of general high school students and further contribute to the vision of human resource development in the 2022 revised curriculum. To this end, the self-management competency prediction variable was analyzed using RandomForest and its importance was derived. Next, the types of self-management competencies were classified through Latent Profile Analysis. In addition, Multinomial Logistic Regression Analysis was conducted to find out whether the self-management competency predictors derived through Random Forest analysis act as influencing factors for categorized profiles through latent profile analysis. The main results of this study are as follows. First, as a result of analyzing the predictive factors of self-management competency of general high school students by applying the random forest technique, it was found that the variables related to "student competency" had the highest predictive power. A total of seven variables related to "student competency," including "metacognition," "behavior control strategy," and "creativity," were identified as the top 20 variables, and the partial dependence chart analyzed that students with higher student competency have higher self-management competencies. In addition, 'attitude to work' and 'friendship' were also analyzed as major predictors with static linear relationships. In addition, class attitude, academic perception, community consciousness, relationship with teachers, appearance perception, and family perception variables were analyzed as major predictors. Next, as a result of latent profile analysis, the types of self-management competencies of general high school students were classified into four potential profiles as a result of latent profile analysis. Each group showed a consistent pattern of class as a group with a sub-factor of self-management competency above the average, a group with an average level, a group below the average, and a group with a lowest level. Finally, for groups classified through latent profile analysis, the top 20 variables derived through random forest were input in units of 10 to verify their influence through multinomial logistic analysis. As a result of the analysis, it was analyzed that the factors that predict self-management competency have different statistical significance and influence directions according to each profile type classification. The main conclusions based on the results of this study are as follows. First, it was found that it was necessary to apply a differentiated method of enhancing self-management competencies according to the level of self-management competencies of general high school students. In the educational field, customized education considering learners' levels and tendencies, such as achievement level, interest, career, and aptitude, is effective in enhancing students' competencies. Therefore, when diagnosing students' level of self-management competency and presenting optimal variables that comprehensively consider the importance index of random forests and the Odds Ratio of multinomial logistic regression analysis to students at that level, it is judged to be most effective for self-management competency. Second, it was found that the self-management competency of general high school students can be effectively improved by comparison and competency. Variables such as academic-related meta-cognition, behavioral control strategies, and attitude or enthusiasm for classes are also important, but the results of this study show that students' social competencies are comparative and competency areas. Lastly, it was analyzed that the self-management competency of general high school students was greatly influenced by external factors of students. It can be seen that the learning strategy and comparison competency-related variables discussed above correspond to internal variables of students in a large framework. In addition to these variables, among the top variables that statically predict self-management competency, the relationship with the teacher and satisfaction with one's appearance were derived as variables that were statistically significant in the self-management competency profile. These results suggest that in addition to improving self-management competencies caused by internal factors of high school students, self-management competencies can be improved by environmental factors.