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This study conducts a comparative analysis of various models for predicting Bitcoin prices. The models utilized in this research include the Gated Recurrent Unit (GRU), Long Short-Term Memory (LSTM), and Random Forest. Their performance was evaluated using key metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R-squared (R²). The findings reveal that the GRU model outperformed the other two models, achieving an R² value of 0.9301, which indicates its superior ability to explain the data. The LSTM model demonstrated relatively high explanatory power with an R² of 0.8252, yet it exhibited lower predictive accuracy compared to the GRU. On the other hand, the Random Forest model recorded the lowest MAE (1557.44) and RMSE (1766.95), reflecting strong short-term prediction capabilities. However, its R² of 0.6110 highlighted limitations in capturing the intricate temporal patterns of the dataset. The significance of this study lies in several aspects. First, it empirically demonstrates that the GRU model, with its more streamlined architecture, can achieve superior predictive performance compared to the LSTM and Rando Forest models. Additionally, while Random Forest showed promising results for short-term predictions, it underscored limitations in modeling the complexities of temporal data patterns. Future research should aim to incorporate broader datasets and integrate external economic variables to enhance predictive accuracy. Furthermore, the potential of ensemble approaches, combining diverse models, holds promise for further improving predictive performance in this domain.