Abstarct: The Foreign Exchange (Forex) market, as the largest financial market worldwide, is characterized by substantial trading volumes, high price volatility, and significant sensitivity to economic and political events. These characteristics make Forex one of the most challenging environments for financial decision-making and investment management. Traditional forecasting and trading approaches often rely on rigid assumptions and predefined models, limiting their ability to adapt effectively to dynamic and uncertain market conditions. This research proposes an intelligent frxamework baxsed on Deep Reinforcement Learning (DRL) for the development of an algorithmic trading system in the Forex market. Within this frxamework, an autonomous agent continuously interacts with the market environment and learns optimal trading strategies through a reward-baxsed learning mechanism. By integrating deep neural networks with reinforcement learning techniques, the proposed model is capable of extracting complex, nonlinear patterns from large-scale financial datasets and addressing the limitations of conventional trading methodologies. The main objective of this study is to design a robust trading system that enhances profitability while maintaining stability and effective risk management under highly volatile market conditions. In addition, critical challenges such as financial data noise, market regime changes, state-space representation, and reward function design are investigated. The results indicate that the proposed approach can improve decision-making performance in algorithmic trading and contribute to the development of intelligent financial systems capable of operating in complex and uncertain environments. .