The incorporation of fundamental ratios for predicting the stock price, such as price to book (PB), price to sales (PS) and Price to earnings(PE), alongside historical price data has gained considerable attention in stock price prediction. This study aims to investigate the effectiveness of utilizing these fundamental ratios in conjunction with Long-Short Term Memory (LSTM) model for predicting stock prices. In particular, we chose three large-cap companies that are listed in the National Stock Exchange (NSE), in India. scripts such as Reliance, Tata Consultancy Services (TCS), and Imperial Tobacco Company (ITC) are selected as case studies. Historical price data and fundamental ratios are collected over a specific period of the past year i.e. march 2022 to march 2023. To ensure accurate predictions, the collected dataset undergoes preprocessing techniques. By incorporating both fundamental ratios and historical price data into LSTM model, this study aims to explore the potential benefits of combining these factors for improved stock price prediction in terms of the chosen metrics like Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE).