This paper introduces a wellness-aware course allocation framework designed to systematically and fairly assign university professors to subjects within an academic department. The primary objective is to address the complex challenge of balancing efficient course coverage, respecting professors’ individual preferences and expertise, and adhering to institutional constraints. The proposed approach incorporates a suitability score derived from professors’ expressed interests and their areas of expertise. An iterative optimization process guided by newly developed fairness metrics–happiness, envy, and wellness–allows the algorithm to progressively refine allocations, achieving high levels of satisfaction and fairness. Experimental evaluations were conducted on two datasets: one representing actual data from the Computer Science Department at an Ecuadorian University (13 professors, 21 subjects), and another larger, synthetic dataset (30 professors, 40 subjects) designed to assess scalability. Results indicate the method’s robustness, with the real world dataset achieving high happiness (0.803) and low envy (0.229), translating into significant overall wellness (0.574). The synthetic dataset demonstrated good scalability, maintaining fairness (envy of 0.1076) despite reduced happiness (0.5146), and increased computational demand. While the method does not guarantee absolute envy-freeness or complete satisfaction due to practical constraints, it effectively balances conflicting objectives, offering improved fairness and satisfaction compared to traditional allocation methods.