In this study, we investigate the use of ensemble machine learning (ML) approaches to anticipate stock market movements, with a focus on the Nifty50 stock. The study uses data from Yahoo Finance to anticipate stock prices to improve prediction accuracy. We have used seven ensemble learning models, including Ada-Boost, Light GBM, XG-Boost, Random Forest (RF), Cat-Boost, Gradient Boosting, and Voting Regressor. Random Forest and Voting Regressor achieve the highest accuracy as compared to another model with less absolute error. This study highlights the possibility of machine learning to provide more accurate stock market predictions. Our thorough planning and assessment of term lengths, feature engineering, and data preprocessing procedures will enable investors to compare stocks of different firms regularly, reducing risk. Additionally, it will enhance the financial and technical aspects of the stock analysis research community.