We investigate a predict-then-optimize method for ship refit project scheduling, integrating machine learning (ML) task duration predictions. Ship refit operations encompass various tasks such as renovation and repair in shipyards, which have become increasingly important recently. Efficient scheduling of these tasks relies heavily on accurate task duration estimates from domain experts. Our study focuses on assessing the impact of using ML algorithms for these estimates on ship refit project schedules, evaluated using a periodic re-optimization approach based on actuals, leveraging a constraint programming model in the optimization process. We compare three methods: expert estimates, historical data forecasting, and augmented human-AI forecasting. Through experimental analysis, we evaluate their performance in predicting task duration and improving the robustness of ship refit project schedules. The results demonstrate the benefit of the augmented human-AI model in providing task duration predictions, yielding more robust ship refit project schedules and limiting the disruptions in resource allocation over the planning horizon. Additionally, the experimental results indicate that the predict-then-optimize approach enhances the robustness of ship refit project schedules, suggesting the approach’s potential to be readily generalized for optimization in other project scheduling domains, such as aviation maintenance.