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When it comes to brick-and-mortar retailing and E-Commerce, anticipating demand accurately is one of the most important aspects in minimizing loss (through unavailability of items) and maximizing profit for any time period. This literature review aims to pursue a deeper understanding in founding a prediction model for forecasting retail sales by using deep learning techniques. The researchers gathered various papers from reputable publishers and institutions and sorted those that dealt with the use of deep learning techniques and machine learning techniques. The researchers found that the most frequent techniques used are Long-Short Term Memory and Random Forest for deep and machine learning respectively. Thus, the researchers proposed a model using LSTM with learning features inherited from common features observed through the related studies gathered and Random Forest as the proposed comparator or baseline model. [ABSTRACT FROM AUTHOR]