The classification of the healthcare personnel is essential for optimizing resource allocation and enhancing service delivery in the medical industry. This study utilized a Decision Tree Classifier to classify healthcare personnel in Kenya into four principal roles: Doctor, Nurse, Technician, and Administrator, based on essential factors including age, experience, and wage level. The dataset, comprising 1,000 records, was created to replicate real-world settings, guaranteeing variation in worker profiles. The preliminary model assessment revealed an accuracy of 81%, signifying inadequate categorization efficacy. After refining the dataset and modifying model parameters, the accuracy dramatically improved to 90%, illustrating the potential of decision trees in workforce classification. Subsequent analysis indicated that experience and salary level were the primary determinants in forecasting job positions. Error analysis revealed misclassification trends, especially within administrative jobs, indicating a necessity for improved feature engineering.