In this study, the authors proposed a new method for constructing an developing an effective model aimed at assessing and predicting fluctuations in the foreign exchange (Forex) market using the Ensemble learning method. Accurately forecasting the trends in Forex trading offers investors more suitable options for their trading decisions. The model is equipped with the capability to automatically process trading parameters from online information sources, allowing it to predict market trends with high accuracy. It integrates time-related features and intelligently optimizes parameters for training, which contributes to its clear stability and high performance. Specifically, the model achieved impressive performance metrics, with an R-squared value of 0.999 and a root mean squared error (RMSE) of 0.00357. This proposal not only offers an effective method for predicting fluctuations in the Forex market but also highlights significant potential for future developments in assessment and prediction models within the currency field.