This research discovers an AI-predictive model that uses a variety of machine learning algorithms to predict the top five finishers in a race. Horse racing is one of those paradigms that presents a challenging dataset against which the race outcomes can be predicted. Horse racing involves numerous variables: horse performance metrics, race conditions, and the jockey, together, decide the outcome of a race. To tackle such complexity, we test several algorithms, including CatBoost, Random Forest, k-Nearest Neighbors, Logistic Regression, Decision Trees, Support Vector Machines, Linear Regression, Naive Bayes, and Gradient Boosting, relating to the incorporation of categorical and continuous data. Our experiments demonstrate that the highest accuracy was achieved with CatBoost, which allows the model to handle categorical features well and is resistant to overfitting. The game theory component supplies useful elements in the strategic interaction between competing horses, thereby further increasing predictive accuracy. Performance metrics—accuracy, precision, and recall were used to estimate each model. The accuracy of CatBoost was found to be 74.1, while others were less accurate. This research provides an important resource for racing stakeholders, from trainers to punters. The research will be valuable in delineating race strategy and which horses are likely to win. This is an advancement in horse racing analytics and lays the foundation for predictive modeling to be explored in similar competitive environments in the future.