Effective workforce management is vital for organizational success, yet inefficient resource allocation often leads to operational bottlenecks, increased costs, and reduced service quality. Traditional workforce planning models struggle with workload variability and skill-based task assignments. This study introduces a Strategic and Systematic workforce pooling methodology to optimize resource utilization by aligning workforce deployment with demand fluctuations and competency requirements. The approach comprises three core components: workforce demand analysis (task classification, skill assessment, workload forecasting), dynamic task orchestration (structured push-and-pull model) and capacity enhancement (cross-functional up-skilling, role adaptability, career growth). A real-world enterprise dataset was analyzed using the proposed model. Workforce clusters were formed through correlation-driven segmentation, for grouping the workforce with similar skill sets and mapping them to operational needs. A multi-model Time-Series Forecasting (TSF) framework, comprising basic models like Simple Moving Average, trend-based models like Holt-Winters, and statistical models like ARIMA, was used to evaluate and select the most suitable predictive model for each team based on the Mean Absolute Error (MAE) metric, achieving an average MAE of ~4% across high-variance demand categories. The implementation led to a 7–10% improvement in workforce utilization over a 6-month period. The resulting methodology offers a data-driven framework for optimizing workforce planning, enhancing agility, and enabling strategic decision-making. While expert judgment remains essential, the model serves as an AI-assisted decision-support system, providing predictive insights that streamline workforce restructuring and proactive talent deployment.