Abstarct: The foreign exchange market, or forex, is the most famous and largest financial market in the world. It is active 24 hours a day, five days a week in different parts of the world. Due to its decentralization and high liquidity, this market has always attracted the attention of traders and investors. However, due to the high fluctuations and the effectiveness of many factors, predicting the price of currencies has always been very challenging. Therefore, there is a need to build an intelligent assistant to facilitate the transaction process. The methods that have been used so far to automate transactions and forecast exchange rates are mainly baxsed on past price changes, and the impact of news and economic events has been given less attention. In the proposed method of this research, raw data related to economic news is extracted from the Forex Factory brokerage. A score is assigned to each currency in different time frxames baxsed on the impact of published news events. Real currency prices are then obtained in 4-hour and one-day periods, and a data set is designed for the intelligent currency price prediction system using this data. Next, several machine learning algorithms, including RandomForest Regression, ARIMA, XGBRegressor, and linear regression, are used to develop an intelligent system for predicting the exchange rate. The evaluation of these models is done using various criteria such as mean absolute error, mean squared error, accuracy, precision, recall, and 1F score. By analyzing the results, it is observed that the model developed with the ARIMA algorithm performed the best, with 71.23% accuracy and a recall rate of 0.981. This high recall rate indicates that the model performs well in recording positive price movements. Additionally, the average absolute error in this model is equal to 0.28, indicating that the model is generally close to the actual values in its price predictions. The findings of this research contribute to the field of forex currency forecasting by demonstrating the effectiveness of using forex factory news in combination with machine learning algorithms. The developed forecasting system, especially with the integration of ARIMA, provides a tool for traders and investors to make informed decisions in the forex market.