Efficiently assigning lecturers to courses is a critical aspect of ensuring both faculty satisfaction and optimal teaching outcomes in Higher Education Institutions. This study introduces an innovative Modified Hungarian Method (MHM) optimization model to address this challenge by incorporating lecturers’ competency scores and preference levels. While previous studies have primarily utilized the traditional Hungarian Method, limited attention has been given to its modified counterpart. Additionally, the application of competency and preference-based criteria in lecturer-to-course assignments remains unexplored. To address these gaps, this research develops a mathematical programming approach to enhance the formulation of the MHM model. The proposed model, referred to as the Competency-Preference Multi-Objective MHM (CP MO-MHM), seeks to achieve two main objectives, maximizing lecturers’ competencies and maximizing their preferences in course assignments. Competency is evaluated through three dimensions that are knowledge, skills and teaching motivation. Data for this study were collected via an online survey of Mathematics lecturers teaching undergraduate courses at UiTM Shah Alam, Malaysia. Using the gathered competency scores and preference levels, the CP MO-MHM model was implemented in MATLAB’s intlinprog function to generate an optimal lecturer-to-course assignment plan, with a maximum limit of three courses per lecturer. The results demonstrate that the CP MO-MHM model effectively identifies the most suitable course assignments for lecturers based on their competencies and preferences. By adapting the MHM framework to integrate these multidimensional inputs, this study contributes a practical tool for improving educational planning. The model not only enhances teaching quality but also minimizes mismatches between lecturers and courses, promoting better academic performance and greater satisfaction among faculty members. This research offers significant advancements in lecturer assignment processes, paving the way for more efficient and effective resource management in academia.