Artificial Intelligence (AI) is rapidly transforming the structure and functioning of financial markets by reshaping trading strategies and the process of price discovery. This study examines the role of AI in Indian financial markets, where algorithmic and AI-assisted trading have moved from niche applications to core market infrastructure. Drawing on recent secondary data from regulatory reports, stock exchanges, and industry studies (2020-2025), the chapter analyses how AI-driven models influence trading behaviour, liquidity, volatility, and market efficiency. The findings indicate that AI-based and algorithmic systems now account for a dominant share of trading activity in India, with over 60% of total trading volume and an even higher share in derivatives. The Indian algorithmic trading market, valued at over USD 560 million in 2024, is projected to grow strongly in the coming years, reflecting increased institutional and gradual retail adoption. AI tools enable high-speed data processing, predictive analytics, and automated execution, allowing faster incorporation of information into prices and thereby improving price discovery. Evidence suggests tighter bid-ask spreads, lower transaction costs, and deeper liquidity in actively traded securities. However, the study also highlights a dual impact. While AI improves efficiency and liquidity under normal conditions, it can amplify short-term volatility during market stress due to synchronized algorithmic responses. Additional concerns include model risk, black-box decision-making, cybersecurity vulnerabilities, and potential market concentration among technologically advanced participants. Regulatory initiatives by SEBI such as algorithm registration norms, audit requirements, and risk controls play a critical role in balancing innovation with investor protection. The study concludes that AI has become a structural driver of modernization in Indian financial markets, enhancing speed, efficiency, and liquidity. Yet, its long-term benefits depend on robust governance, transparency, and responsible AI frameworks. Future research should focus on empirical measurement of AI’s long-run effects and the development of explainable AI systems for inclusive and stable markets.