Abstract: Machine learning-based techniques are described that enable determining an optimal timing schedule for retries of a failed event such as a failed payment transaction. A payment retry optimization machine learning model is trained using ground-truth payment outcome data to obtain a trained model configured to identify an optimal timing schedule for one or more retries of a failed payment transaction based on various inputs including a time of the failed payment transaction, a set of payment attributes, an overall payment retry period, and a maximum number of retries to be attempted.