Background: Burn injuries present a significant global health challenge. Among the most severe long-term consequences are contractures, which can lead to functional impairments and disfigurement. Understanding and predicting the evolution of post-burn wounds is essential for developing effective treatment strategies. Traditional mathematical models, while accurate, are often computationally expensive and time-consuming, limiting their practical application. Recent advancements in machine learning, particularly in deep learning, offer promising alternatives for accelerating these predictions. Methods: This study explores the use of a deep operator network, a type of neural operator, as a surrogate model for finite element simulations aimed at predicting post-burn contraction across multiple wound shapes. A deep operator network was trained on three distinct initial wound shapes, with enhancements made to the architecture by incorporating initial wound shape information and applying sine augmentation to enforce boundary conditions. Findings: The performance of the trained deep operator network was evaluated on a test set including finite element simulations based on convex combinations of the three basic wound shapes. The model achieved an R² score of 0.99, indicating strong predictive accuracy and generalization. Moreover, the model provided reliable predictions over an extended period of up to one year, with speedups of up to 128-fold on the Central Processing Unit and 235-fold on the Graphical Processing Unit, compared to the numerical model. Interpretation: These findings suggest that deep operator networks can effectively serve as a surrogate for traditional finite element methods in simulating post-burn wound evolution, with potential applications in medical treatment planning.