Financial stock values are non-linear, volatile, and chaotic, making them one of the most challenging financial time series to predict. The incentive of financial gain has led many researchers and academia to devise methods to predict the stock market, despite copious uncertainty. Because of their ability to recognize complex patterns in several applications, machine learning models are extensively researched among the most recent methods. In this paper, Support Vector Machine, Artificial Neural Networks, and Case-based Reasoning for stock market prediction are surveyed. This paper also reviews sentiment analysis to highlight the behavioral trends of the stock market and its investors with the advent of technology. A generalized modeling methodology for applying machine learning techniques to the stock market is proposed in this paper.