Abstract: Techniques are provided for machine learning-based prediction of completion time of software code changes. One method comprises obtaining events related to software code changes; applying the events to a machine learning model that predicts a completion time of the software code changes, wherein the machine learning model is trained using (i) events for historical software code changes and (ii) a completion time for each historical software code change; and performing a remedial action based on the predicted completion time. The remedial action may comprise generating a notification, and/or adjusting an allocation of resources assigned to a completion of the software code changes. The software code changes can be monitored and a new event related to the software code changes can be applied to the machine learning model to obtain an updated predicted completion time for the changes to the software code.