Artificial intelligence (AI)-based CV recommendation systems leverage advanced algorithms to analyze candidate profiles, including skills, experience, and preferences, alongside employer requirements to provide tailored recommendations. These systems significantly enhance recruitment efficiency by reducing the time required for candidate screening and mitigating human biases. Recent advancements in deep learning, natural language processing (NLP), and recommender system technologies have further improved the accuracy and adaptability of these tools. This paper provides an overview of AI-driven CV recommendation systems, their methodologies, and the advantages they offer in strategic decision-making for recruitment. Moreover, we explore various AI techniques, such as embedding-based models, cosine similarity, and deep learning approaches, to evaluate their effectiveness in matching candidates with job vacancies. Finally, we discuss the potential challenges and future research directions in refining AI-driven profile recommendation systems.