This study looks at the usage of cutting-edge AI as well as ML technologies for forecasting of stock market, with an emphasis on short-term projections for notable US-listed businesses in a range of industries. Our program seeks to generate directional forecasts by utilizing historical price data, technical indicators, and sentiment analysis of news. We explore a variety of stock market analytical topics, such as risk assessment, pattern detection, and machine learning-based investment return estimates. Using historical data, the paper thoroughly investigates the Efficient Market Hypothesis and its consequences on stock price prediction. The efficacy of many methods and models, including LSTM networks, ARIMA, and GARCH, in financial prediction is assessed. We also go over issues with using technology-driven forecasting techniques, including data scarcity, overfitting, and moral dilemmas.