#AITradingAffectsForex Machine learning (ML) is transforming Forex prediction by enabling traders to make more accurate and data-driven decisions. Unlike traditional statistical models, ML algorithms can analyze vast amounts of historical and real-time data to detect complex patterns and trends that humans might miss. Supervised learning models, such as Support Vector Machines (SVM) and Random Forests, are commonly used for classification and regression tasks, predicting future currency price movements based on historical data. Meanwhile, unsupervised learning techniques, such as clustering, help identify hidden structures within large datasets, improving decision-making. Deep learning, a subset of ML, offers even greater predictive capabilities. Neural networks, particularly Long Short-Term Memory (LSTM) networks, are highly effective for time series analysis, making them ideal for predicting currency price movements. Moreover, reinforcement learning allows models to continuously adapt and improve based on feedback from their trading performance. This adaptability makes ML particularly powerful in dynamic Forex markets, where conditions can change rapidly. The application of ML in Forex prediction enhances trading accuracy, minimizes risks, and provides traders with valuable insights into market behavior. As machine learning technology advances, its influence on Forex trading is expected to grow, making predictive models even more sophisticated and reliable.