The stock market is crucial for a country’s economy. It reflects the economic health and investment status of a country. While it has attracted the interest of many scholars, the volatility of stock prices and the indicators influencing this volatility has not been extensively studied, particularly using classification techniques. This study aims to fill this gap in the literature by identifying an effective classification technique to classify the data of BRICS countries using eight classification techniques via WEKA software from 2000 to 2021. Additionally, the study seeks to explore the common indicators that significantly impact stock price volatility in BRICS countries. Findings reveal that tree algorithm-based techniques performed well in terms of accuracy and reliability, although no single common classification technique was identified. Among the eight techniques, Random Tree classified the data of BRICS countries with high accuracy, except for India, where the J48 technique was more efficient. Furthermore, the study indicates that there are no common indicators affecting stock price volatility, as these indicators vary across countries due to the distinct economic and sociopolitical structures of BRICS countries. These findings provide valuable insights for investors and policymakers to better understand and manage stock market dynamics in BRICS countries.