Store Item Demand Forecasting Using Deep Learning and Data Analytics is used to accurately forecast store item demand so that retailers can maximize inventory levels, cut expenses, and improve customer happiness. A user-friendly web interface built on Flask that makes forecasts and insights easily accessible and promotes an organizational culture that is data-driven. The system analyzes past sales data, promotional activity, and external factors like economic situations using sophisticated deep learning techniques, especially Artificial Neural Networks (ANNs). In order to categorize things into high, medium, and low demand groups and predict future demand trends, these inputs are processed. The main technological component of this strategy is Artificial Neural Networks (ANNs), which take advantage of their capacity to simulate intricate. Multiple layers, including input, hidden, and output layers, make up artificial neural networks (ANNs), which process a variety of information and spot complex patterns in demand behavior. The artificial neural network (ANN) model guarantees dynamic adaptation to shifting market conditions by integrating temporal dependencies and real-time data streams. Over time, forecasting accuracy is maintained and model obsolescence is avoided by unannounced retraining processes utilizing updated datasets. Deep learning, data analytics, and realtime processing work together to increase operational effectiveness and profitability while giving retailers vital insights into item demand. The approach encourages a data-driven organizational culture that places a high value on strategic decision-making in demand planning and inventory management. The project's goal is still to improve retail performance and customer satisfaction by giving companies the resources they need to efficiently track, forecast, and control demand in their operational environment.