The study by A Study on AI and ML in Stock Index Prediction to Investors provides the researcher with the opportunity to explore how AI and ML can be used to predict the trends of major stock indexes in India including NIFTY 50, SENSEX, NIFTY BANK, FIN Nifty, and NIFTY AUTO. The study is pertinent because it addresses the growing complexity, volatility and non-linearity of financial markets in which traditional analytical instruments tend to fail in making accurate predictions. The main goals of the study include determining the level of effectiveness of AI/ML models in stock index movement prediction, determining the significant technical and financial aspects that affect the accuracy of predictions and investment decisions, and suggesting a more sophisticated hybrid AI and ML model that would yield higher forecasts and investment decisions. The secondary sources of data used to base the study will include the historical data of the stock index of the National Stock Exchange, Bombay Stock Exchange, and the financial websites like Yahoo Finance. The variables in the dataset are the opening, high, low and closing prices and also the trading volume. It also displays technicals like Moving Averages, Relative Strength Index, Moving Average Convergence Divergence and volatility. Various tools and methods are used in the research, such as Python-based ML models, such as Support Vector Machines, Random Forest, k-Nearest Neighbours , and Deep Learning models, including Long Short-Term Memory and Gated Recurrent Unit .The performance of the models is analysed by such statistical parameters as Accuracy, Precision, Recall, Mean Absolute Error , Root Mean Squared Error  and R 2 score. The findings have shown that most of the regression models tend to be ineffective, have high error rates, and negative R 2, implying that they are weak predictors due to the complex and dynamic nature of stock markets. Models such as kNN and Random Forest are more efficient in some indices but the overall accuracy is not so high to be able to make valid predictions. Lastly, the article points out that despite the fact that AI and ML techniques can be successfully implemented to stock market, the effectiveness of the methodology lies in proper feature engineering, model optimization, and data of different types.