The research aims to theoretically substantiate the application of robust clustering algorithms to enhance the effectiveness of teaching methodologies for the Russian language. The authors explore the potential use of mathematical algorithms for predicting student behavior. The problem of forecasting learning activity related to the mastery of linguistic material necessitates the use of analytical tools capable of identifying differences among learners. This approach creates opportunities for designing individualized educational trajectories that consider the cognitive and behavioral characteristics of schoolchildren, which vary depending on the dominance of the right or left hemisphere of the brain. The methodological foundation of this research comprises fundamental research on hemispheric asymmetry and higher mental functions, as well as machine learning theory. The primary research method involves robust clustering algorithms, which allow for more accurate classification of students based on data analysis, including academic performance, task completion time, and common grammatical errors. Methods based on Mahalanobis distance facilitate the selection of individualized Russian language learning trajectories that align with specific didactic tasks set by the teacher. The research opens new perspectives in Russian language teaching methodologies by integrating mathematical modeling to improve the quality of philological education and adapt to the contemporary demands of the digital society. The research is original in that it establishes a connection between the cognitive approach in Russian language teaching methodologies and the application of robust clustering algorithms for predicting student behavior.