Cryptocurrency is a form of digital currency that relies entirely on electronic transactions and does not have a counterpart in the form of traditional banking system. Unlike fiat money, it operates in a decentralized manner without the intervention of third parties and allows users to access services directly. However, fluctuations in exchange rates significantly affect international trade and relations and contribute to global economic imbalances. Advance trading knowledge will help the traders in taking timely and appropriate decision . Though it is tough task to predict the price in advance, accurate predictions can be done up to some extent. This study focused on predicting the price of Bitcoin, a popular cryptocurrency that is widely accepted by various stakeholders including investors, researchers, traders, and policy makers by using various machine learning models. Therefore, this study proposes to compare the performance of two machine learning methods (RNN model and LSTM model) with a hybrid machine learning model (ANN-PSO) for Bitcoin price prediction. Additionally, the study contributes to sustainable cities and communities by comparing the real value of the two models with closed prices .