Predicting stock prices accurately is difficult owing to the numerous elements that might influence stock prices, such as economic indicators, news events, and market patterns. Conventional approaches to stock price prediction frequently depend on statistical models that may be incapable of capturing the complicated interactions between these variables. Deep learning approaches like Long Short-Term Memory (LSTM) networks have showed promise in understanding these complicated linkages and effectively forecasting stock values in recent years. We conduct a literature review on deep learning-based techniques for stock price prediction in this study. We give an overview of deep learning techniques and how they may be used to forecast stock prices. We investigate publically available datasets for training and evaluating deep learning models for stock price prediction. We also show a case study in which we anticipate the stock values of many businesses listed on the National Stock Exchange of India using an LSTM-based model. Our findings show that the LSTM-based model outperforms traditional approaches to stock price prediction, showing that deep learning techniques have the potential to be a valuable tool for investors looking to make educated stock investing decisions.