This paper presents a time series demand prediction model designed to forecast Bitcoin price fluctuations. As cryptocurrency markets are known for their high volatility and speculative nature, accurate predictions are crucial for investors and financial analysts. Utilizing advanced machine learning techniques, this study builds and evaluates multiple models, including ARIMA, LSTM, and Prophet, to predict Bitcoin prices based on historical data. The models are compared for accuracy, computational efficiency, and their ability to capture trends and seasonality. The research demonstrates that incorporating external variables, such as trading volume and market sentiment, can significantly enhance prediction accuracy. This study contributes to the growing field of cryptocurrency forecasting by offering insights into the most effective methodologies for predicting Bitcoin prices, addressing both technical challenges and practical implications for real-world applications.