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The increasing demand for skilled professionals in competitive job markets has highlighted the need for effective interview preparation tools. Traditional methods of interview practice often lack personalization, real-time feedback, and scalability. To address these limitations, this paper presents an intelligent, AI-driven mock interview system designed to simu- late real-world interview environments and enhance candidate readiness. The system leverages advanced technologies such as natural language processing (NLP), machine learning, and speech analysis to conduct interactive interview sessions across multiple domains. The proposed platform dynamically generates questions based on user-selected roles, skill levels, and domains, ensuring a tailored interview experience. It evaluates user responses in real-time by analyzing linguistic quality, confidence, fluency, and relevance. Additionally, the system provides detailed feedback including performance scores, improvement suggestions, and behavioral insights, enabling users to identify their strengths and weaknesses effectively. The integration of voice recognition and automated evaluation enhances realism and user engagement. The architecture is designed to be scalable and user-friendly, supporting seamless interaction through a web-based interface with a database for tracking user progress and enabling adaptive learning. Experimental results demonstrate that the proposed system significantly improves user confidence, communication skills, and overall interview performance. This solution can be widely applied in academic institutions, training centers, and recruitment platforms. Future enhancements may include emotion detection, multilingual support, and deeper AI-driven personalization to further improve accuracy and user experience.