This survey paper explores the evolving role of Artificial Intelligence in advancing factor investing, a financial strategy that leverages specific security characteristics to generate alpha. Traditional factor investing has focused on well-established factors such as value, momentum, and quality, but Artificial Intelligence introduces transformative enhancements across various stages of the investment process. By applying machine learning techniques, Artificial Intelligence enables the processing of large datasets, the identification of complex patterns, and dynamic adaptation to market changes, addressing key limitations of traditional approaches. This paper examines AI-driven advancements in factor investing, including reinforcement learning for adaptive portfolio management and algorithmic trading for efficient trade execution. It also discusses critical challenges such as overfitting, data quality, and model interpretability, emphasizing the potential of AI to refine investment strategies. The findings suggest that integrating AI into factor investing enhances precision and adaptability in portfolio management while fostering innovation in asset management. Future research should focus on improving model interpretability and mitigating overfitting to optimize the synergy between Artificial Intelligence and factor investing in complex financial markets.