This study examines the application of machine learning models in managing healthcare workforce allocation to meet fluctuating demands. Four models—Logistic Regression, Decision Tree, Random Forest, and Support Vector Machine—are evaluated for their accuracy and reliability. The Random Forest model achieves the highest accuracy of 0.98, followed by Decision Tree, Logistic Regression, and Support Vector Machine. The results highlight the models' potential in enhancing operational efficiency and addressing staffing challenges. By utilizing historical and real-time data, these models can predict staffing needs, optimize resource allocation, and improve patient care outcomes. The study emphasizes the importance of adopting machine learning techniques in healthcare workforce management to achieve a more responsive and efficient system.