Many organizations use business processes as a tool to realize and sustain competitive advantage in the market. A business process is a structured collection of activities with comprehensible sequence and dependency to yield a required outcome. The optimization of these processes is of paramount importance because optimized processes yield adaptability, accurate information, enhanced efficiency, accountability through performance monitoring, and improved quality. Relying on business people such as executives and management to identify areas of improvement in the business processes is potentially subjective. This research commences on the assumption that business processes are fully constituted for a business and on this premise seeks an alternate, none subjective, optimization technique. A Decision Tree (DT) is a tool that supports decision making by means of a tree-structured modeling approach to map possible outcomes of a chain of interconnected choices. When applied in statistical regression modeling, a DT model employs supervised learning techniques to model decisions in a tree structure with possible results, input costs, and usefulness. In a DT model, aspects of an element are monitored and the model is trained to predict the future. DT can be applied to improve business processes by identifying activities or elements with significant impact when enhanced. This paper demonstrates business process optimization via DT regression modeling by the use of Python programming.