Software maintenance and support efforts consume a significant amount of the software project budget to operate the software system in its expected quality. Manually estimating the total hours required for this phase can be very time-consuming, and often differs from the actual cost that is incurred. The automation of these estimation processes can be implemented with the aid of machine learning algorithms. The maintenance and support effort prediction models need to be explainable so that project managers can understand which features contribute to this model outcome. This study contributes to the development of the maintenance and support effort prediction model using various tree-based regression machine-learning techniques from cross-company project information. The developed models were explained using the state-of-the-art model agnostic technique SHapley Additive Explanations (SHAP) to understand the significance of features from the developed model. This study concluded that staff size, application size, and number of defects are major contributors to the maintenance and support effort prediction models.