Objective: This study tackles the reviewer assignment problem by proposing a model that optimizes reviewer-proposal matching based on thematic coverage, fairness, and expertise, while considering workload balance and team size constraints. The model incorporates practical constraints such as limits on the number of proposals each reviewer can handle and team composition requirements. This approach is especially relevant to institutions like academic conferences, journals, and funding organizations, aiming to enhance the integrity and efficiency of the review process.Methods: This study is classified as descriptive research with a practical orientation and relies on data collection through applied methods. The approach is grounded in mathematical modeling. Initially, the selected articles are grouped into clusters. Reviewers are then assigned to these clusters using a multi-objective binary integer programming model that incorporates all relevant criteria and constraints. To implement this model, 150 articles were selected through purposive sampling. The model was optimized using Python, employing both the branch-and-bound algorithm and a genetic metaheuristic algorithm to maximize the degree of reviewer-proposal matching within the proposed framework. Results: The proposed model demonstrates strong practical relevance by closely reflecting real-world reviewer assignment challenges. By simultaneously optimizing thematic coverage, evaluation fairness, and reviewer expertise, the model captures the complexity of actual allocation scenarios. To validate its effectiveness, the model was solved using both the branch-and-bound algorithm and a genetic algorithm. The branch-and-bound method yielded an objective value of 177.349 in approximately one hour, while the genetic algorithm reached 120.35 in just seven minutes. Although branch-and-bound guarantees optimality, its longer runtime makes it less practical for larger datasets. Given the similarity of results, the genetic approach is a reliable and scalable alternative.Conclusion: This study introduces a new allocation strategy and mathematical model for reviewer assignment, addressing often-overlooked factors such as reviewer expertise, grouping, and conflicts of interest. By integrating these elements, the proposed model better reflects real-world conditions. Future work is encouraged to expand on these findings with new frameworks and methods.