Accurate prediction of foreign exchange (forex) rates is essential for informed decision-making in international trade, investment, and risk management. Traditional econometric models often struggle to capture the complex, non-linear patterns inherent in forex markets. This study investigates the application of deep learning techniques to forecast exchange rate movements and support comprehensive market analysis. We develop and evaluate multiple deep neural network architectures, including Long Short-Term Memory (LSTM) networks and convolutional neural networks (CNNs), to model temporal dependencies and extract salient features from historical price data and macroeconomic indicators. Empirical results across major currency pairs demonstrate that deep learning models outperform conventional time series forecasting methods in terms of prediction accuracy and robustness. Additionally, feature importance analysis highlights key drivers influencing exchange rate volatility. The findings underscore the potential of deep learning as a valuable tool for enhancing forex market analysis, risk assessment, and automated trading strategies.