U.S. financial markets grow more intricate each day, so investors turn to machine learning models for better market volatility predictions. This research reviews Machine Learning models that predict market volatility by looking at their approaches and measuring how effectively they forecast volatility. The review starts with the examination of traditional time-series models and demonstrates their weaknesses in recognizing complex market volatility movements. The analysis then focuses on advanced Machine Learning models. Each model’s strengths and weaknesses are scrutinized in the context of volatility forecasting. The paper explores the pivotal role of feature selection and engineering in enhancing the predictive power of ML models for volatility forecasting. Feature sets encompassing financial indicators, macroeconomic variables, sentiment analysis from news articles, and social media data are analyzed for their impact on forecasting accuracy. The review examines how accurate these prediction models are at spotting market volatility. This review explains how machine learning models predict market volatility by showing the various methods used while discussing their strengths and weaknesses. The insights from our research benefit Machine Learning scientists, business experts, and investors who plan to use Machine Learning methods to better handle market volatility. The outcome of this research points towards the need for continual developments in machine learning models for better prediction of volatility.