<p>Coronary Heart Disease, a common cause of death globally, has a debilitating effect on the individual, society, and the economy. Hence, screening and treating Coronary Heart Disease is critical. This paper proposes a risk prediction method of Coronary Heart Disease based on a new machine learning model, which combines a teaching–learning Seagull Optimization Algorithm with the Lightboost based Gradient Boosting Machine. The beta distribution in the Seagull Optimization Algorithm initializes the population to maximize the enveloped optimal solution. A variable convergence factor is applied to balance the global search and local search speed by using a logistic function to improve the algorithm’s search performance. A teaching–learning strategy is integrated to enhance the diversity of the seagull population and the quality of the seagulls during the seagull position updates to avoid local optimality. The teaching–learning Seagull Optimization Algorithm is used to search for the best combination of the main hyperparameters of Lightboost based Gradient Boosting Machine model. An empirical analysis is conducted using the Coronary Heart Disease data set on the Kaggle platform. The result shows that the Accuracy, The Positive Rate, True Negative Rate, F1-score, G-mean and Area Under Curve value of the proposed paper are 0.8978, 0.9333, 0.9388, 0.8934, 0.9361 and 95.74% respectively, which performs better than the other compared machine learning models. This validates the feasibility and superiority of the proposed model in risk prediction of Coronary Heart Disease.</p>