This paper investigates the impact of Artificial Intelligence (AI) on trading strategies in financial markets, comparing AI-driven approaches with traditional methodologies and their effects on market efficiency, liquidity, and volatility. It critically examines how AI challenges established financial theories, such as the Efficient Market Hypothesis and behavioral finance, suggesting a potential redefinition of market dynamics considering AI’s superior data processing and analytical capabilities. The study synthesizes academic literature, theoretical insights, and speculative analysis to assess the multifaceted implications of AI’s integration into trading practices. Findings highlight AI-driven strategies’ enhanced risk-adjusted returns and contribution to market efficiency, yet underscore a complex impact on market dynamics, where AI can both improve liquidity and introduce volatility. Ethical considerations and regulatory challenges are emphasized, pointing to the need for transparent and adaptive regulatory frameworks to address the opacity of AI decision-making and ensure market integrity. The paper advocates for interdisciplinary research and collaboration among technologists, regulators, and market participants to navigate the evolving landscape of AI in trading. Through this exploration, the study contributes to the discourse on AI-driven trading, balancing the benefits of technological advancements against the risks and challenges, and underscores the critical role of regulatory oversight in shaping the future of financial markets.