Stock market analysis and prediction have been one of the super centric domains among computing researchers. The availability of data and the nasty behavior of the stock market is the major parameter for attracting the computing research in the field. A huge amount of data is available openly for various stock markets among the globe such as the European Stock Market, Tehran, Nasdaq, Nifty/BSE and many more. Various data mining and machine learning techniques have evolved as prominent algorithms for analyzing and predicting the stock market. In this paper, various such techniques are minutely studied to understand the overall working of the stock market with the various top techniques such as SVR, SVM, Neural Networks, Fuzzy, Decision Trees, Genetic Algorithm, Classification, Clustering etc. Though a lot of research has been done in this area but still it has not been exploited to its best level, for identifying those pitfalls and to follow desired approach the paper also focuses on opportunity for future scope in this domain to help upcoming researchers. © 2019 IEEE.