The stock market, sometimes known as the share market, is a marketplace for stock sellers and buyers. A stock exchange is defined as a mechanism through which stockbrokers can purchase and sell shares, bonds, and other assets. Many businesses, regardless of their industries or domains, make their shares or stocks available on the stock exchange. For value investors, predicting the price direction of a company is crucial. Due to its volatile nature, forecasting the stock market is a challenging task. Accurate stock forecasting is essential for building important trading systems that help customers purchase and sell equities. Due to the dynamic nature and nonstationary nature of data, stock price prediction and modeling is a difficult endeavor. By decreasing investment risks, developing a successful stock prediction system would assist shareholders in making profitable investment decisions. Using deep learning-based methodologies, this research provides a deep learning-based strategy for dramatically boosting stock forecasting accuracy. This study is a comparison between the results of LeNet and the proposed model for the prediction of Iraqi stock marketing for five years (from 2017 to 2021). The results of the proposed model demonstrate the average training accuracy is 99%, the average validation accuracy is 95%, the average training loss is 0.28% and for validation, loss is 0.014%.