Predictive modeling approaches have garnered substantial interest in predicting price fluctuations of cryptocurrencies, especially Bitcoin, due to their volatile nature. Deep learning models in particular, which are machine learning algorithms, have become extremely effective instruments for financial forecasting in current time. In order to judge bitcoin prices, this paper compares the performance of convolutional neural networks (CNNs) such VGG16 and VGG19, Gated Recurrent Unit (GRU) and Long Short-Term Memory (LSTM). As input features, the study makes use of previous Bitcoin price data and pertinent market indicators. Recurrent neural networks, such as LSTM and GRU, are popular choices for time series forecasting jobs because they perform well in sequential data analysis. However, by using pre-processing techniques, VGG16 and VGG19—which were first created for picture classification—can now interpret temporal sequences of price data. The Bitcoin price time series temporal dependencies are captured using LSTM and GRU, which are well-known for their efficacious modeling of sequential data. A variety of metrics, including as MAE and MSE, are used to determine each model’s behavior. The study also looks at each algorithm’s computational effectiveness and training duration. The discoveries of this ponder contribute to the developing body of writing on cryptocurrency cost expectation by advertising a comprehensive comparison of LSTM, GRU, VGG16, and VGG19 calculations. The results provide insightful advice for researchers and practitioners who want to use machine learning methods to predict Bitcoin prices and navigate the ever-changing cryptocurrency marketplaces.