In the present period of time, when advancement is reaching unprecedented heights, every business and organization is struggling to manage both its inventory and client expectations. Sales are the main source of every business, having a general knowledge of what to expect in the future which aids in making smart, strategic sales decisions. Since revenue forecasting is typically the starting point for creating annual budgets for the business, majority of companies are still confused about how to do it. A company's estimation may be negatively impacted over time if its sales projections are regularly off. As a result, sales forecasting has an impact on the entire corporation to enhance their whole growth plan. Sale forecasting is a crucial component of every company's sales operations. An effective sales forecast is crucial for a company to provide the required quantity at the proper time. The predictions are used by executives to plan for enterprise expansion and to evaluate the future performance. In this study, we attempt to forecast the sales of a retailing company by employing the machine learning approaches, Linear regression and Naive Forecasting. A computational example is presented to show the difference between the linear regression and Naïve forecasting methods and we have discovered that the Linear regression produces superior outcomes when compared to the Naïve forecasting approaches. Also we have predicted the sales for the next 5 days by using ARIMA model for the linear regression approach.