One crucial part of modeling infectious disease dynamics is accurate parameter estimation. This study proposes a framework using physics-informed neural networks with metaheuristic hyperparameter tuning to estimate parameters in infectious disease models. For practical applicability, the method uses only infected case data while enforcing the governing differential equations during training. The results shows that the approach can effectively recover model parameters from limited observations.