Stock market prediction has entered a phase of technological advancement with the introduction of amazing technical innovations like global digitization. This new model is remodeling the previous form of trading. Due to rising market capitalization, ceaselessly, stock trading has emerged as a core investment for most financial investors. Several techniques and tools have been developed by various analysts and researchers that calculate the stock price and provide sound decisions to investors (Bhavana M, Priyadarshini P, Om B, Sheetal H in Stock market prediction using machine learning. In: 2024 5th international conference for emerging technology (INCET), Karnataka, India, pp 1–6 (2024). https://doi.org/10.1109/INCET61516.2024.10593215 ). The researcher can now forecast market with social nontraditional textual data sites thanks to advanced trading models. Advanced machine learning techniques including ensemble methods and text data analytics have significantly improved prediction accuracy. However, research in this field of stock market analysis and prediction is strongly complicated since the data characterizes inherent dynamic, erratic, and sometimes chaotic behavior. It describes the systematics framework in a generic manner for machine learning based approaches towards stock market prediction. Critical analysis of results using the contributions from the past decade, from 2011–2021, is done, which were gathered data from electronic digital libraries and databases through the ACM digital library and Scopus. To identify the significant trend, a thorough comparative analysis was also carried out. Researchers in developing fields could benefit from the study by better understanding the foundations and developments in the field and, as a result, be able to conduct promising researches.