Ensemble learning techniques enhance currency trading by combining multiple machine learning models to improve prediction accuracy and robustness. Instead of relying on a single model, ensemble methods aggregate insights from various algorithms, reducing the risk of overfitting and improving generalization in volatile Forex markets. Popular ensemble methods include bagging, boosting, and stacking. Bagging, used in models like Random Forest, trains multiple decision trees on different subsets of Forex data to reduce variance and enhance stability. Boosting, as seen in Gradient Boosting Machines (GBM) and XGBoost, sequentially corrects prediction errors by focusing on difficult patterns in currency price movements. Stacking combines the predictions of diverse models, such as neural networks, support vector machines (SVMs), and decision trees, to generate a final, more accurate forecast. Ensemble learning in Forex helps traders improve trend prediction, volatility forecasting, and risk management. Despite challenges like increased computational complexity, these techniques offer more reliable and adaptive trading strategies in dynamic financial markets.