This study intends to investigate several machine learning algorithms for sales forecasting strategies. A retailer can use this to predict future market demand and adjust its inventory levels accordingly. The accuracy of these predictions will determine whether the retailer profits or suffers losses. In this paper, we worked on the Walmart Sales dataset from Kaggle. It has over 400,000 rows and about 20 columns. After cleaning and performing the necessary feature engineering of the data, we used machine learning algorithms such as eXtreme Gradient Boosting (with and without tuned hyperparameters), Linear Regression, Ridge Regression, Decision Tree Regressor and Random Forest Regressor (with and without tuned hyperparameters). The most effective algorithm out of all the others was XGBoost when the hyperparameters were tuned. This model performs well on sales prediction by utilising less processing power and memory.