In recent years, Bitcoin has emerged as a prominent digital asset, attracting significant attention from investors and researchers alike due to its volatile nature and potential for substantial returns. Predicting the price movements of Bitcoin is a challenging yet crucial task for investors seeking to make informed decisions. In this study, we employ Long Short-Term Memory (LSTM), a type of recurrent neural network (RNN), to forecast the future prices of Bitcoin. LSTM networks are well-suited for sequential data analysis, making them ideal for modeling the time-series nature of cryptocurrency prices. By leveraging historical Bitcoin price data, along with relevant market indicators, we train the LSTM model to learn patterns and trends in the data. Additionally, we explore various features, such as trading volume, sentiment analysis from social media, and macroeconomic factors, to enhance the predictive capabilities of our model. Through rigorous experimentation and evaluation, we demonstrate the effectiveness of LSTM in accurately forecasting Bitcoin prices over different time horizons. We evaluate the model's performance using standard metrics such as Mean Absolute Error (MAE) and Root Mean Square Error (RMSE), providing insights into its predictive accuracy and reliability. Furthermore, we conduct comparative analyses with other traditional forecasting methods to highlight the superior performance of LSTM in capturing the complex dynamics of Bitcoin price movements. Our findings contribute to the growing body of research in cryptocurrency price prediction and offer valuable insights for investors and market analysts seeking to navigate the volatile landscape of digital assets.