This paper presents a hybrid machine learning framework that addresses scalability and accuracy challenges in retail inventory management by integrating real-time demand forecasting with anomaly detection, evaluated using Walmart’s historical sales data. Traditional approaches face a trade-off: maintaining individual models for each product category is computationally prohibitive, while generalized models often underperform for dissimilar items, resulting in stock outs or overstocking. To address this, we propose a department-level aggregation strategy that balances specificity and generalization, combined with a hybrid methodology: ARIMA for linear trend and seasonality modeling, cubic spline interpolation to capture nonlinear residual patterns, and neural networks for complex interactions. The framework dynamically adjusts predictions using real-time sales streams and applies residual-based anomaly detection with threshold triggers to identify sudden demand spikes or supply disruptions. Experiments on a filtered Walmart dataset (12 months, 15 departments) indicate an 18% reduction in mean absolute error (MAE) compared to exponential smoothing baselines, while spline-enhanced neural networks achieve a 24% improvement over standalone ARIMA. The anomaly detection module identifies 92% of simulated irregularities with a 7% false-positive rate. The proposed framework provides three principal advantages: (1) scalable department-level modeling without per-product customization, (2) real-time adaptability to fluctuating demand, and (3) cost-efficient inventory optimization through integrated anomaly alerts. This work offers a practical blueprint for retailers to enhance forecasting precision, mitigate supply chain risks, and reduce operational costs in volatile markets.