Abstract : This study developed an intelligent decision-support tool, the Student Track Recommendation System Using Data-Driven Methods, to identify the best specialization track fit for incoming third-year Information and Communications Technology students based on their theoretical and practical examination results. Realizing that the choice of specialization is an essential academic decision that often receives limited guidance and thus relies heavily on subjective judgment, this system will analyze patterns of student performance using data-driven techniques, namely collaborative filtering and content-based filtering, to develop personalized, objective recommendations. To achieve this goal, it involves data gathering, preprocessing, algorithmic modeling, system design, and implementation to ensure the system supports structured, evidence-based analysis. This systematic assessment of students' competencies will help improve academic decision-making, support the faculty and the administration in determining readiness for specialization, and foster a more transparent, data-informed guidance process. The present study aims to contribute to the field of educational technology by demonstrating the value of implementing data-driven methodologies to facilitate improved student placement and further align academic pathways with demonstrated strengths. Other variables to be used in further development include soft skills, experiential learning, and labor market insights, to further increase the precision of recommendations and the system's adaptability.