The rapid growth of financial technologies (FinTech) and the increasing accessibility of financial markets to retail investors have paved the way for the development of Robo-Advisor systems. By leveraging artificial intelligence algorithms and financial data analytics, these systems provide portfolio management services at lower costs and higher speeds compared to traditional human financial advisors. Despite significant advancements in the field of Robo-Advisors, most existing systems are designed based on classical financial theories and assume that investors behave in a fully rational manner. However, behavioral finance studies have demonstrated that investors'' decisions are influenced by numerous cognitive and emotional biases, which may lead to deviations from rational decision-making.The primary objective of this research is to develop a novel framework for designing a personalized Robo-Advisor that incorporates not only investors’ financial characteristics and risk tolerance but also their behavioral biases into the decision-making process. In this framework, Deep Reinforcement Learning (DRL) serves as the core decision-making mechanism, enabling the intelligent agent to learn optimal asset allocation strategies through continuous interaction with the market environment and analysis of user behavior. By integrating market data, investor-specific characteristics, and behavioral indicators, the proposed model can generate investment recommendations tailored to the unique profile of each investor.The findings of the theoretical analysis suggest that integrating behavioral finance concepts with deep reinforcement learning algorithms can enhance the performance of Robo-Advisors in portfolio management, risk control, and investor satisfaction. Furthermore, the proposed system is expected to mitigate the adverse effects of behavioral biases such as overconfidence, loss aversion, herd behavior, and anchoring, thereby facilitating more rational and effective investment decisions.