<p>Supply chain inventory management is an essential yet demanding activity with parameters such as uncertain customer demand, time-varying lead times, and unexpected production or shipping disruptions. Traditional models like newsvendor and economic order quantity provide the basis for modeling strategies but are just insufficient when dealing with the real-world uncertainties. In this research, a cutting-edge demand forecasting model based on the Red Panda Optimizer with Logistic Mapping (RPO-LM) is proposed to support better inventory decisions. The methodology proposes three basic phases: data preparation, model training, and model evaluation. The prediction accuracy is improved by normalizing historical inventory data and splitting them into training and test datasets. The RPO-LM model is trained to reflect short-term fluctuation and long-term trends in demand to remain adaptive to dynamic inventory levels. To ascertain the accuracy of the predictions, performance analysis is carried out using metrics such as mean absolute percentage error and root mean squared error. The proposed model supports anticipatory inventory management, minimizing stockouts and overstocking, thereby maximizing supply chain effectiveness. Python simulations validate the effectiveness of the suggested strategy, demonstrating its superiority over more conventional approaches such as the least squares polynomial sinusoidal method and auto regressive integrated moving average. With a mean absolute percentage error of around 4 and a root mean squared error of about 6, the suggested approach has the highest forecast accuracy. Outcomes verify that proposed method greatly enhances prediction accuracy and economic efficiency, qualifying it as an optimal solution for contemporary inventory control systems.</p>