AI-driven feature selection is crucial in Forex trading, helping traders and machine learning models identify the most relevant variables for predicting currency price movements. With vast amounts of financial data available, including historical prices, macroeconomic indicators, interest rates, and geopolitical events, selecting the right features improves model accuracy and efficiency. Machine learning techniques such as Recursive Feature Elimination (RFE), Principal Component Analysis (PCA), and autoencoders help reduce dimensionality by selecting only the most informative features. AI models, including deep learning and reinforcement learning, analyze correlations and dependencies between variables, ensuring that the selected features contribute to meaningful predictions. In Forex trading, feature selection enhances trend forecasting, volatility analysis, and risk management by eliminating redundant or noisy data. AI-driven methods also adapt dynamically to market changes, ensuring continuous optimization. Despite challenges like overfitting and data biases, AI-powered feature selection significantly improves algorithmic trading strategies, allowing traders to make more data-driven and profitable decisions.