This study explores the impact of artificial intelligence (AI) on stock price prediction in India, focusing on the application of Long Short-Term Memory (LSTM) networks and hybrid models. By analyzing data from the Bombay Stock Exchange (BSE) including stock prices, trading volumes, and sentiment data from social media and news, the study compares AI-based models with traditional methods. The research aims to determine whether AI models offer superior predictive accuracy using RMSE, MAE, and MAPE. The findings suggest that AI models, particularly LSTM and hybrid approaches, outperform traditional models in forecasting accuracy, offering a significant advantage for stock market prediction in India. The study concludes AI models provide a significant improvement in stock price prediction accuracy compared to traditional methods. This suggests the adoption of AI models in stock price prediction by Indian financial institutions and regulatory bodies. This study provides valuable insights for investors, financial institutions, and policymakers looking to integrate AI into market forecasting strategies.

Artificial intelligence, stock price prediction, India, LSTM model, financial forecasting