The intersection of artificial intelligence with financial markets is a revolutionary change in how we perceive and engage with economic systems. This review discusses the state of affairs with AI-based stock market forecasting, integrating literature on machine learning applications, economic effects, and wider societal consequences. AI models prove highly adept in identifying patterns in financial prediction, but their large-scale deployment poses sophisticated trade-offs between market stability and efficiency. High-frequency trading algorithms control market volume, and deep learning models handle varied streams of data from conventional financial indicators to alternate sources such as social media sentiment. These advances introduce new challenges, though: flash crashes become more common, regulatory environments are confounded by algorithmic opacity, and wealth concentration may be exacerbated. From interdisciplinary studies across organizational management, consumer conduct, crisis management, and technological innovation, this paper uncovers that the influence of AI goes beyond profits in trading to questions of capital distribution, employment patterns, and economic fairness. The examination illustrates that triumph demands not only technological advancement but prudent deployment balancing innovation and risk, human discretion and algorithmic power, and productivity and system stability.