This research work aims to fill the existing literature by outlining and attempting to solve specific employee retention issues in the engineering profession in the Sultanate of Oman and the larger Gulf region using machine learning algorithms. To solve the problem of resource allocation in engineering organizations, we have created a decision model that predicts Employee Retention based on the other features: The four factors include (1) Employee Experience, (2) Employee Skills, (3) Project Complexity, and (4) Resource Availability. With the help of Random Forest classifiers, we provide a pattern that one could use to enhance retention measures as well as efficient utilization of resources. The obtained results show that in Class 0 (Not Retained), the model achieves 85% Precision, 80% Recall, and an F1-Score of 82%. For Class 1 (Retained), it records 87% Precision, 90% Recall, and an F1-Score of 88%. Overall, the model effectively identifies employee retention status with balanced performance across both classes. This work will help engineering organizations in the Sultanate of Oman and the Gulf region to make the right decisions on how to retain their star employee which, in turn, will improve productivity and ultimately engender organizational effectiveness.