Stock market forecasting has always been a significant task. Its importance lies in the fact that predicting stock rates successfully helps us gain attractive profits through wise decisions. Although the prediction helps us earn profits yet, it’s a major challenge due to blaring, uncertain, non-stationary, and non-linear financial market behaviour. Many techniques help to predict stock market trends. This study reviewed approximately 100 research papers that suggested methodologies, like Artificial Neural Networks (ANN), Support Vector Machine (SVM), Fuzzy Classifiers, Machine Learning Methods, and so on, based on stock market prediction. Few efforts have been undertaken to list the research gaps and the challenges faced by the existing techniques, which help the researchers to upgrade the future works. The works are analyzed using the bibliometric approach carried out between 1975 and 2021 using Scopus data, thereby highlighting the citation patterns and involving journals, authors, and countries in forecasting the stock market. Furthermore, keywords analysis is also staged using VOSViewer and Bibliometrix tools to visualize the research patterns. Despite a lot of research efforts, the current stock market prediction techniques still have many limits. This survey finally, tries to conclude that the stock market prediction is a very complex task, and different factors should be taken into account for predicting the future of the market more accurately and efficiently.