In emerging economies, predicting the price of the stock is essential for taking investment decisions. This paper summarizes the machine learning techniques applied in the earlier literature for stock market prediction. This paper focuses on the leading research, strategies and methods used, insights drawn, concepts to be explored, experimental evidence yet to be developed and new challenges in the area of stock price prediction using machine learning techniques over the past ten years through a methodical analysis of 22 research publications from various databases (2014–2024). This paper looks at five significant issues: determining the effectiveness of machine learning algorithms for stock price prediction, the impact of data preprocessing and feature engineering, model evaluation of stocks, its applications and challenges using a systematic survey. The end of this research finds the need for artificial intelligence tools for prediction of market value to improve the accuracy of the results.