Following the conclusion of the Paris Olympic Games, global focus has gradually turned toward the upcoming Olympic events, among which the final medal table remains a widely concerned topic worldwide. This study integrates historical Olympic medal data, host effects, and athlete-related factors to establish a Random Forest model and a linear regression model, aiming to forecast the total medal and gold medal numbers of various countries, explore the distribution laws of Olympic medals, and put forward strategies for improving national medal performances. Based on host effects, historical performance, and recent athletic achievements, the Random Forest model is applied to predict the total medals and medal rankings of each country in the 2028 Los Angeles Olympics, with corresponding prediction intervals given. The study further identifies countries with potential medal increases or decreases. The linear regression model quantifies the impact of elite coaches on medal results and offers targeted advice for different countries on coach introduction. Two perspectives are examined: the host effect and the "great coach" effect. Long‑term data from 1960 to 2024 is used to quantify the host effect through medal growth rates, while sensitivity analysis based on feature importance is conducted to verify model rationality. The results show that the United States, as the 2028 host country, will maintain a strong competitive advantage. The established models possess practical value and can provide decision support for the International Olympic Committee and national Olympic committees in resource allocation and strategic planning.

Cao Shengyang. Prediction of Future Olympic Medal Table Dominance Using Random Forest and Linear Regression Models. Academic Journal of Computing & Information Science (2026), Vol. 9, Issue 4: 1-8. https://doi.org/10.25236/AJCIS.2026.090401.

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