A Stock Market is a place where company shares are traded to stockbrokers. Stock price prediction is one of the most challenging problems as a high level of accuracy is the key factor in predicting a stock market. Many methods are used to predict the price in the stock market but none of those methods are proven as a consistently acceptable prediction tool due to their volatile nature. The stock market prediction has been one of the popular topics in the financial domain. Prior to the advancement in Machine Learning and Artificial Intelligence, many Statistical models were employed. Even though these models gave almost accurate results but using these models was not efficient and time-consuming because of the market’s rapidly changing behavior. Due to the advancement in Machine Learning and Artificial Intelligence, it has become possible to employ different models like Neural Networks, Regression, Decision Trees, etc. which give us results as accurate results as Statistical Models if not better in a very short amount of time. This paper, going to make use of an artificial neural network (ANN) model with backpropagation to forecast stock prices. ANN can generalize and predict data after learning and analyzing the initial inputs and their relationships. A feed-forward network and backward propagation algorithm is used to predict stock prices. This research work introduced a method that can find out the future value of stock prices on a particular day and also predict a bullish or bearish market based on the current price using the ANN backpropagation algorithm.