Effective management of inventory is one of the primary concerns for any industry or enterprise in Supply Chain Management. Sales Forecasting is one of the most critical tools in pursuing this for these industries. Researchers have employed a mixture of conventional and deep learning models. However, existing sales forecasting models need more comprehensiveness and flexibility to account for the dynamics and nonlinearities in re-tail-level sales time series. We have implemented a hybrid deep neural network model to overcome those challenges. This methodology allows the visualisation of product sales, promotional events, and weekend or holiday effects. A large volume of sales data from a fast food chain company is utilized to validate our model. This dataset contains multidimensional features—both indigenous to the dataset and de-rived. First, we perform exploratory data analysis to extract meaningful information from the sales data. Then, we infer seasonal effects and the impact of holidays, weekends, and marketing on sales. We applied the Parallel CNN-GRU algorithm, considering all the factors to predict the demand for the fast food chain company and minimizing its loss by reducing wastage. A comparative study is executed with various state-of-the-art sales forecasting techniques to showcase/confirm the significance of our model. We have also analyzed our proposed model's complexity compared with other existing models.