This paper introduces a novel methodology that integrates Machine Learning (ML), Operations Research (OR), and Game Theory (GT) to develop an interpretable heuristic for principal-agent models (PAM). We extract solution patterns from ensemble tree models trained on solved instances of a PAM. Using these patterns, we develop a hierarchical tree-based approach that forms an interpretable ML-based heuristic to solve the PAM. This method ensures the interpretability, feasibility, and generalizability of ML predictions for game-theoretic models. The predicted solutions from this ensemble model-based heuristic are consistently high quality and feasible, significantly reducing computational time compared to traditional optimization methods to solve PAM. Specifically, the computational results demonstrate the generalizability of the ensemble heuristic in varying problem sizes, achieving high prediction accuracy with optimality gaps between 1--2% and significant improvements in solution times. Our ensemble model-based heuristic, on average, requires only 4.5 out of the 9 input features to explain its predictions effectively for a particular application. Therefore, our ensemble heuristic enhances the interpretability of game-theoretic optimization solutions, simplifying explanations and making them accessible to those without expertise in ML or OR. Our methodology adds to the approaches for interpreting ML predictions while also improving numerical tractability of PAMs. Consequently, enhancing policy design and operational decisions, and advancing real-time decision support where understanding and justifying decisions is crucial. / Master of Science / This paper introduces a new method that combines Machine Learning (ML) with Operations Research (OR) to create a clear and understandable approach for solving a principal-agent model (PAM). We use patterns from a group of decision trees to form an ML-based strategy to predict solutions that greatly reduces the time to solve the problem compared to traditional optimization techniques. Our approach works well for different sizes of problems, maintaining high accuracy with very small differences in objective function value from the best possible solutions (1-2%). The solutions predicted are consistently high quality and practical, significantly reducing the time needed compared to traditional optimization methods. Remarkably, our heuristic typically uses only 4.5 out of 9 input features to explain its predictions, making it much simpler and more interpretable than other methods. The results show that our method is both efficient and effective, with faster solution times and better accuracy. Our method can make complex game-theoretic optimization solutions more understandable, even for those without expertise in ML or OR. By improving the interpretability making PAMs analytically explainable, our approach supports better policy design and operational decision-making, advancing real-time decision support where clarity and justification of decisions are essential.