Abstarct: The stock market is a place where company shares and other securities are bought and sold. This market allows investors to invest in companies and benefit from their profits. The advantages of investing in the stock market include the potential for high returns and quick liquidity. However, it also has disadvantages such as high risk and market volatility. Linear regression is used in the stock market to predict stock prices baxsed on historical data. This method helps analysts identify price trends and patterns. The present study examines a method for predicting stock markets using linear regression. This system uses machine learning knowledge to determine the future price of the market for investment. Linear regression is a statistical method used to model the relationship between a dependent variable and one or more independent variables. In this method, a straight line is fitted to the data to predict the relationship between the variables. This method helps analysts identify data trends and patterns and make accurate predictions. In this study, we used linear regression in stock markets to identify the future of each asset in the stock market using past data. Our goal was to use specific techniques in linear regression in the financial market. By using various regression methods and techniques to limit past data, we were able to achieve desirable results. However, predicting the financial market with linear regression has limitations. This method only models linear relationships and cannot identify more complex relationships. It is also sensitive to outliers and cannot predict sudden market fluctuations. To address these limitations, we used certain techniques to achieve good results. By using these techniques, we succeeded in completing our transactions with 99% accuracy in multiple regression and weighted regression, while simple and polynomial regression did not yield good results in different trading data.