We investigate the use of deep learning models for retail sales forecasting in this research. Proper sales forecasting can lead to optimization in inventory management, marketing strategies, and other core business operations. This research evaluates deep learning models such as Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Multilayer Perceptron (MLP), and a hybrid CNN-LSTM model. The models are further improved by adding dense layers to process daily sales data from a major pharmaceutical company. The models are trained on 80% of the dataset and tested on the remaining 20%. Model performance is compared using Root Mean Square Error (RMSE) and Mean Absolute Error (MAE).... More >