We investigate the use of supervised machine learning models to predict equilibrium outcomes in a predator–prey system with disease. Epidemics in this system are described by a coupled model that integrates Lotka–Volterra (LV) predator–prey dynamics with a Susceptible–Infected–Susceptible (SIS) epidemic framework. A synthetic dataset is generated by randomly varying model parameters within prescribed ranges and recording the final susceptible and infected populations as equilibrium states. This dataset is then used to train a wide suite of supervised models, including a linear model, tree ensembles, boosting methods, and neural networks. The results demonstrate that machine learning provides fast and reliable equilibrium prediction, enabling large-scale scenario exploration without repeatedly integrating the underlying system of ordinary differential equations.