Meta-Ensemble Machine Learning Architecture for Predictive Workforce Allocation and Organizational Decision Intelligence

The allocation of the workforce is a complicated task of the contemporary business where dynamic projects requirements and the variety of skills along with changing resources limits interact. In order to overcome these complexities, the current research proposes a Meta-Ensemble Machine Learning Architecture that can predict and optimize the deployment of workforce in an intelligent manner. The model capitalizes on the strengths of other algorithms by utilizing the complementary advantages of each algorithm by combining Random Forest, XGBoost, SVM, and Bi-LSTM as base learners and utilizing the results through a meta-learner using a stacking. Input data has employee profiles, workload history, skill rating and productivity metrics, which guarantee the comprehensive picture of organizational dynamics. Multiobjective optimization layer based on the use of NSGA-II finetunes workforce allocation after prediction, and balances skilltask fit, utility, and urgency in time limits. The model showed a better performance and the highest accuracy of 96.47 % and Strategy 6 and 9 had a higher utilization of over 93%, which proves the practicality of the framework. Moreover, SHAPbased interpretability allows making understandable decisions made by the model, which develops trust with stakeholders in the enterprise. The system is built to scale based on enterprise data flows in real-time, and change based on project requirements. This combination of ensemble prediction and prescriptive optimization enables the organizations to have agile and explainable decision intelligence. The future extensions can take additional external labor trends and economic indicators, which would make them more broadly applicable to dynamic, data-driven enterprise ecosystems.