We construct sophisticated AI and machine learning models to forecast disease outcomes and epidemic trajectories with greater accuracy and fairness. A central tenet of our work is the reliance on properly collected and curated real-world datasets, which helps to minimize the biases often present in conventional models. Our research includes developing models for disease diagnostics and prognostics, as well as creating agent-based and behaviorally-aware simulations to predict epidemic spread in heterogeneous populations, ultimately informing more effective and equitable public health strategies.