In the field of financial analytics, stock market prediction continues to be one of the most difficult and sought-after objectives. A key component of stock price modeling and forecasting is time series analysis, a statistical technique that examines sequences of data points gathered at successive times. A thorough review of time series analytic techniques for stock market prediction is given in this article. These techniques include machine learning and deep learning, as well as more sophisticated approaches like GARCH and ARIMA. It addresses the drawbacks and advantages of these methods, looks at the difficulties in putting them into practice, and identifies new developments in time series forecasting. Investors and analysts may improve their ability to anticipate the future and make better judgments in the ever-changing stock market environment by being aware of these techniques and how they are used.